Gene expression profiles to predict breast cancer outcomes

ABSTRACT

Methods for classifying and for evaluating the prognosis of a subject having breast cancer are provided. The methods include prediction of breast cancer subtype using a supervised algorithm trained to stratify subjects on the basis of breast cancer intrinsic subtype. The prediction model is based on the gene expression profile of the intrinsic genes listed in Table 1. This prediction model can be used to accurately predict the intrinsic subtype of a subject diagnosed with or suspected of having breast cancer. Further provided are compositions and methods for predicting outcome or response to therapy of a subject diagnosed with or suspected of having breast cancer. These methods are useful for guiding or determining treatment options for a subject afflicted with breast cancer. Methods of the invention further include means for evaluating gene expression profiles, including microarrays and quantitative polymerase chain reaction assays, as well as kits comprising reagents for practicing the methods of the invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/931,597 filed Nov. 3, 2015. Ser. No. 14/931,597 is a divisional ofU.S. patent application Ser. No. 12/995,450, filed Nov. 30, 2010. U.S.Ser. No. 12/995,450 is a National Stage Application, filed under 35U.S.C. § 371 of International Application No. PCT/US2009/045820, filedJun. 1, 2009, which claims priority under 35 U.S.C. § 119 to U.S.Provisional Application Ser. No. 61/057,508, filed May 30, 2008. Thecontents of the aforementioned applications are incorporated herein byreference in their entireties.

GOVERNMENT INTEREST

This invention was made with government support under grant numbers R01CA095614, U01 CA114722, and P50 CA582230 awarded by The NationalInstitutes of Health. The government has certain rights in theinvention.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has beensubmitted in ASCII format via EFS-Web and is hereby incorporated byreference in its entirety. Said ASCII copy, created on Mar. 13, 2019, isnamed “NATE-702_C01US.txt” and is 20,274 bytes in size.

FIELD OF THE INVENTION

The present invention relates to methods for classifying breast cancerspecimens into subtypes and for evaluating prognosis and response totherapy for patients afflicted with breast cancer.

BACKGROUND OF THE INVENTION

Breast cancer is the second most common cancer among women in the UnitedStates, second only to skin cancer. A woman in the U.S. has a one ineight chance of developing breast cancer during her lifetime, and theAmerican Cancer Society estimates that more than 178,480 new cases ofinvasive breast cancer will be reported in the U.S. in 2007. Breastcancer is the second leading cause of cancer deaths in women, with morethan 40,000 deaths annually. Improved detection methods, mass screening,and advances in treatment over the last decade have significantlyimproved the outlook for women diagnosed with breast cancer. Today,approximately 80% of breast cancer cases are diagnosed in the earlystages of the disease when survival rates are at their highest. As aresult, about 85% percent of breast cancer patients are alive at leastfive years after diagnosis. Despite these advances, approximately 20% ofwomen diagnosed with early-stage breast cancer have a poor ten-yearoutcome and will suffer disease recurrence, metastasis or death withinthis time period.

Significant research has focused on identifying methods and factors forassessing breast cancer prognosis and predicting therapeutic response(See generally, Ross and Hortobagyi, eds. (2005) Molecular Oncology ofBreast Cancer (Jones and Bartlett Publishers, Boston, Mass.) and thereferences cited therein). Prognostic indicators include conventionalfactors, such as tumor size, nodal status and histological grade, aswell as molecular markers that provide some information regardingprognosis and likely response to particular treatments. For example,determination of estrogen (ER) and progesterone (PgR) steroid hormonereceptor status has become a routine procedure in assessment of breastcancer patients. See, for example, Fitzgibbons et al., Arch. Pathol.Lab. Med. 124:966-78, 2000. Tumors that are hormone receptor positiveare more likely to respond to hormone therapy and also typically growless aggressively, thereby resulting in a better prognosis for patientswith ER+/PgR+ tumors. Overexpression of human epidermal growth factorreceptor 2 (HER-2/neu), a transmembrane tyrosine kinase receptorprotein, has been correlated with poor breast cancer prognosis (see,e.g., Ross et al., The Oncologist 8:307-25, 2003), and HER-2 expressionlevels in breast tumors are used to predict response to the anti-HER-2monoclonal antibody therapeutic trastuzumab (Herceptin®, Genentech,South San Francisco, Calif.).

SUMMARY OF THE INVENTION

Methods for classifying and for evaluating prognosis and treatment of asubject with breast cancer are provided. The methods include predictionof breast cancer subtype using a supervised algorithm trained tostratify subjects on the basis of breast cancer intrinsic subtype. Theprediction model is based on the gene expression profile of theintrinsic genes listed in Table 1. In some embodiments, the algorithm isa nearest centroid algorithm, similar to the Prediction Analysis ofMicroarray (PAM) algorithm. The algorithm can be trained based on dataobtained from the gene expression profiles deposited as accession numberGSE10886 in the National Center for Biotechnology Information GeneExpression Omnibus. This prediction model, herein referred to as thePAM50 classification model, can be used to accurately predict theintrinsic subtype of a subject diagnosed with or suspected of havingbreast cancer.

Further provided are compositions and methods for predicting outcome orresponse to therapy of a subject diagnosed with or suspected of havingbreast cancer. These methods are useful for guiding or determiningtreatment options for a subject afflicted with breast cancer. Methods ofthe invention further include means for evaluating gene expressionprofiles, including microarrays and quantitative polymerase chainreaction assays, as well as kits comprising reagents for practicing themethods of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A to 1C shows outcomes based on subtype predictions using thePAM50 classifier. The PAM50 classification for LumA, LumB,HER2-enriched, Basal-like, and Normal-like shows prognostic significancefor 1451 patients across all 5 test sets combined (FIG. 1A), in 376patients given endocrine therapy alone (FIG. 1B), and in 701 nodenegative patients given no adjuvant systemic therapy (FIG. 1C).

FIGS. 2A to 2D shows risk classification for test cases using a fullmodel of intrinsic subtypes and two clinical variables. (FIG. 2A) riskof relapse scores plotted for each breast cancer subtype: low riskscores <−0.1, moderate risk scores between −0.1 and 0.2, and high riskscores ≥0.2. (FIG. 2B) Kaplan-Meier plots and significance of the riskscore for all 1286 test samples, (FIG. 2C) 376 patients that receivedadjuvant endocrine therapy only, and (FIG. 2D) 560 patients that werenode-negative and received no adjuvant systemic therapy.

FIGS. 3A and 3B shows the linear score for prognosis using thesubtype-clinical model for risk of relapse at 5 years. Linear fit with95% confidence intervals calibrates the risk of relapse score. Thecontinuous risk model with subtype and clinical variables (T and N) wascalibrated from 657 patients with ER-positive early stage breast cancer(FIG. 3A), and in 1286 patients with ER-positive and ER-negative diseaseand stage 1-3 (FIG. 3B).

FIGS. 4A and 4B shows association of PAM50 intrinsic subtype, determinedby qPCR from paraffin blocks, with (FIG. 4A) relapse free survival and(FIG. 4B) disease-specific survival among 702 women with invasive breastcarcinoma treated with adjuvant tamoxifen.

FIGS. 5A and 5B shows Kaplan-Meier analysis of breast cancerdisease-specific survival for patients stratified into low, medium andhigh risk categories by applying the Risk-Of-Relapse algorithm to qPCRdata generated from paraffin blocks. (FIG. 5A) ROR-S, (FIG. 5B) ROR-C.

FIGS. 6A and 6B shows Kaplan-Meier analysis of breast cancerdisease-specific survival for patients stratified into low, medium andhigh risk categories (as defined previously on independent material byapplying the ROR-C algorithm to women with (FIG. 6A) node negativedisease, and (FIG. 6B) node positive disease).

FIGS. 7A to 7D shows Kaplan-Meier analysis of breast cancerdisease-specific survival for patients stratified into node negative ornode positive categories among women with a low risk ROR-C (FIG. 7A),women with moderate risk ROR-C (FIG. 7B); women with high risk ROR-C(FIG. 7C). For direct comparison, all curves are superimposed in thelower right panel. Among women with low ROR-C, there is no significantdifference in outcome by nodal status (FIG. 7D).

FIG. 8 shows that an analysis of the ROR-C model versus probability ofsurvival, stratified by the number of involved lymph nodes, reveals goodoutcomes regardless of nodal status category among patients with ROR-Cvalues less than 25, who have overlapping 95% confidence intervals(denoted by dashed lines)

FIG. 9 shows the results of Kaplan-Meier analysis that was performedseparately on each Adjuvant risk group, and differences in survivalbetween the 90-95% and the 95-100% risk groups were tested using thelog-rank test.

FIGS. 10A to 10C shows the results of Kaplan-Meier analysis that wasperformed separately on each Adjuvant risk group, and differences insurvival between (FIG. 10A) the Adjuvant Predicted BCSS 80-90%, ROR-SLow vs. Med/High; (FIG. 10B) the Adjuvant Predicted BCSS 70-80%, ROR-SLow vs. Med/High; and, (FIG. 10C) Adjuvant Predicted BCSS<70%, ROR-S Lowvs. Med/High.

DETAILED DESCRIPTION OF THE INVENTION Overview

Despite recent advances, the challenge of cancer treatment remains totarget specific treatment regimens to distinct tumor types withdifferent pathogenesis, and ultimately personalize tumor treatment inorder to maximize outcome. In particular, once a patient is diagnosedwith cancer, such as breast cancer, there is a need for methods thatallow the physician to predict the expected course of disease, includingthe likelihood of cancer recurrence, long-term survival of the patientand the like, and select the most appropriate treatment optionsaccordingly.

For the purposes of the present invention, “breast cancer” includes, forexample, those conditions classified by biopsy or histology as malignantpathology. The clinical delineation of breast cancer diagnoses iswell-known in the medical arts. One of skill in the art will appreciatethat breast cancer refers to any malignancy of the breast tissue,including, for example, carcinomas and sarcomas. Particular embodimentsof breast cancer include ductal carcinoma in situ (DCIS), lobularcarcinoma in situ (LCIS), or mucinous carcinoma. Breast cancer alsorefers to infiltrating ductal (IDC) or infiltrating lobular carcinoma(ILC). In most embodiments of the invention, the subject of interest isa human patient suspected of or actually diagnosed with breast cancer.

Breast cancer is a heterogeneous disease with respect to molecularalterations and cellular composition. This diversity creates a challengefor researchers trying to develop classifications that are clinicallymeaningful. Gene expression profiling by microarray has provided insightinto the complexity of breast tumors and can be used to provideprognostic information beyond standard pathologic parameters (1-7).

Expression profiling of breast cancer identifies biologically andclinically distinct molecular subtypes which may require differenttreatment approaches [van't Veer 2005][Loi 2007][Cheang 2008a]. Themajor intrinsic subtypes of breast cancer referred to as Luminal A,Luminal B, HER2-enriched, Basal-like have distinct clinical features,relapse risk and response to treatment [Sorlie 2003]. The “intrinsic”subtypes known as Luminal A (LumA), Luminal B (LumB), HER2-enriched,Basal-like, and Normal-like were discovered using unsupervisedhierarchical clustering of microarray data (1, 8). Intrinsic genes, asdescribed in Perou et al. (2000) Nature 406:747-752, are statisticallyselected to have low variation in expression between biological samplereplicates from the same individual and high variation in expressionacross samples from different individuals. Thus, intrinsic genes are theclassifier genes for breast cancer classification. Although clinicalinformation was not used to derive the breast cancer intrinsic subtypes,this classification has proved to have prognostic significance (1, 6, 9,10).

Breast tumors of the “Luminal” subtype are ER positive and have asimilar keratin expression profile as the epithelial cells lining thelumen of the breast ducts (Taylor-Papadimitriou et al. (1989) J Cell Sci94:403-413; Perou et al (2000) New Technologies for Life Sciences: ATrends Guide 67-76, each of which is herein incorporated by reference inits entirety). Conversely, ER-negative tumors can be broken into twomain subtypes, namely those that overexpress (and are DNA amplified for)HER-2 and GRB7 (HER-2-enriched) and “Basal-like” tumors that have anexpression profile similar to basal epithelium and express Keratin 5,6B, and 17. Both these tumor subtypes are aggressive and typically moredeadly than Luminal tumors; however, there are subtypes of Luminaltumors with different outcomes. The Luminal tumors with poor outcomesconsistently share the histopathological feature of being higher gradeand the molecular feature of highly expressing proliferation genes.

The translation of the intrinsic subtypes into a clinical assay has beenchallenging because unsupervised clustering is better suited toorganizing large numbers of samples and genes than classifyingindividual samples using small gene sets.

Thus, provided herein are improved methods and compositions forclassifying breast cancer intrinsic subtypes. The methods utilize asupervised algorithm to classify subject samples according to breastcancer intrinsic subtype. This algorithm, referred to herein as thePAM50 classification model, is based on the gene expression profile of adefined subset of intrinsic genes that has been identified herein assuperior for classifying breast cancer intrinsic subtypes, and forpredicting risk of relapse and/or response to therapy in a subjectdiagnosed with breast cancer. The subset of genes, along with primersspecific for their detection, is provided in Table 1.

In some embodiments, at least about 40 of the genes listed in Table 1are used in the PAM50 classification model. In other embodiments, atleast 41, at least 43, at least 44, at least 45, at least 46, at least47, at least 48, at least 49, or all 50 of the intrinsic genes listed inTable 1 are used in the model. The methods disclosed herein are notintended for use with one or only a few of the genes listed in Table 1.In fact, it is the combination of substantially all of the intrinsicgenes that allows for the most accurate classification of intrinsicsubtype and prognostication of outcome or therapeutic response totreatment. Thus, in various embodiments, the methods disclosed hereinencompass obtaining the genetic profile of substantially all the geneslisted in Table 1. “Substantially all” may encompass at least 47, atleast 48, at least 49, or all 50 of the genes listed in Table 1. Unlessotherwise specified, “substantially all” refers to at least 49 of thegenes listed in Table 1. It will also be understood by one of skill inthe art that one subset of the genes listed in Table 1 can be used totrain an algorithm to predict breast cancer subtype or outcome, andanother subset of the genes used to characterize an individual subject.Preferably, all 50 genes are used to train the algorithm, and at least49 of the genes are used to characterize a subject.

TABLE 1 PAM50 Intrinsic Gene List REPRE- SENTA- TIVE GENBANK ACCES- SEQSEQ GENE SION FORWARD ID REVERSE ID NAME NUMBER PRIMER NO: PRIMER NO:ACTR3B NM_ AAAGATTCCTG  1 TGGGGCAGTTCTG  51 020445NM_ GGACCTGA TATTACTTC001 040135 ANLN NM_ ACAGCCACTTT  2 CGATGGTTTTGTA  52 018685 CAGAAGCAAGCAAGATTTCTC BAG1 NM_ CTGGAAGAGTT  3 GCAAATCCTTGGG  53 004323 GAATAAAGAGCCAGA BCL2 NM_ TACCTGAACCG  4 GCCGTACAGTTCC  54 000633 GCACCTG ACAAAGGBIRC5 NM_ GCACAAAGCCA  5 GACGCTTCCTATC  55 001012271 TTCTAAGTC ACTCTATTCBLVRA BX647539 GCTGGCTGAGC  6 TTCCTCCATCAAG  56 AGAAAG AGTTCAACA CCNB1NM_ CTTTCGCCTGA  7 GGGCACATCCAGA  57 031966 GCCTATTT TGTTT CCNE1BC035498 GGCCAAAATCG  8 GGGTCTGCACAGA  58 ACAGGAC CTGCAT CDC20 BG256659CTGTCTGAGTG  9 TCCTTGTAATGGG  59 CCGTGGAT GAGACCA CDC6 NM_ GTAAATCACCT10 ACTTGGGATATGT  60 001254 TCTGAGCCT GAATAAGACC CDCA1 NM_ GGAGGCGGAAG11 GGGGAAAGACAA  61 031423 AAACCAG AGTTTCCA CDH3 BC041846 GACAAGGAGAA 12ACTGTCTGGGTCC  62 TCAAAAGATCA ATGGCTA GC CENPF NM_ GTGGCAGCAGA 13GGATTTCGTGGTG  63 016343 TCACAA GGTTC CEP55 AB091343 CCTCACGAATT 14CCACAGTCTGTGA  64 GCTGAACTT TAAACGG CXXC5 BC006428 CATGAAATAGT 15CCATCAACATTCT  65 GCATAGTTTG CTTTATGAACG CC EGFR NM_ ACACAGAATCT 16ATCAACTCCCAAA  66 005228 ATACCCACCAG CGGTCAC AGT ERBB2 NM_ GCTGGCTCTCA17 GCCCTTACACATC  67 001005862 CACTGATAG GGAGAAC ESR1 NM_ GCAGGGAGAGG 18GACTTCAGGGTGC  68 001122742 AGTTTGT TGGAC EXO1 NM_ CCCATCCATGT 19TGTGAAGCCAGCA  69 130398 GAGGAAGTATA ATATGTATC A FGFR4 AB209631CTTCTTGGACC 20 TATTGGGAGGCAG  70 TTGGCG GAGGTTTA FOXA1 NM_ GCTACTACGCA21 CTGAGTTCATGTT  71 004496 GACACG GCTGACC FOXCl NM_ GATGTTCGAGT 22GACAGCTACTATT  72 001453 CACAGAGG CCCGTT GPR160 AJ249248 TTCGGCTGGAA 23TATGTGAGTAAGC  73 GGAACC TCGGAGAC GRB7 NM_ CGTGGCAGATG 24 AGTGGGCATCCCG 74 005310 TGAACGA TAGA HSPC150 NM_ GGAGATCCGTC 25 AGTGGACATGCGA  75(UBE2T) 014176 AACTCCAAA GTGGAG KIF2C NM_ TGGGTCGTGTC 26 CACCGCTGGAAAC 76 006845 AGGAAAC TGAAC KNTC2 NM_ CGCAGTCATCC 27 CGTGCACATCCAT  77006101 AGAGATGTG GACCTT KRT14 BC042437 ACTCAGTACAA 28 GAGGAGATGACCT  78GAAAGAACCG TGCC KRT17 AK095281 GTTGGACCAGT 29 GCCATAGCCACTG  79CAACATCTCTG CCACT KRT5 M21389 TGTGGCTCATT 30 CTTCGACTGGACT  80 AGGCAACCTGT MAPT NM_ GACTCCAAGCG 31 CAGACATGTTGGT  81 001123066 CGAAAACATTGCACATT MDM2 M92424 CCACAAAATAT 32 AGGCGATCCTGGG  82 TCATGGTTCTTAAATTAT G MELK NM_ CCAGTAGCATT 33 CCCATTTGTCTGT  83 014791 GTCCGAGCTTCAC MIA BG765502 GTCTCTGGTAA 34 CTGATGGTTGAGG  84 TGCACACT CTGTTMKI67 NM_ GTGGAATGCCT 35 CGCACTCCAGCAC  85 002417 GCTGACC CTAGAC MLPHNM_ AGGGGTGCCCT 36 TCACAGGGTCAAA  86 024101 CTGAGAT CTTCCAGT MMP11 NM_CGAGATCGCCA 37 GATGGTAGAGTTC  87 005940 AGATGTT CAGTGATT MYBL2 BX647151AGGCGAACACA 38 TCTGGTCACGCAG  88 CAACGTC GGCAA MYC NM_ AGCCTCGAACA 39ACACAGATGATGG  89 002467 ATTGAAGA AGATGTC NAT1 BC013732 ATCGACTGTGT 40AGTAGCTACATCT  90 AAACAACTAGA CCAGGTTCTCTG GAAGA ORC6L NM_ TTTAAGAGGGC41 CGGATTTTATCAA  91 014321 AAATGGAAGG CGATGCAG PGR NM_ TGCCGCAGAAC 42CATTTGCCGTCCT  92 000926 TCACTTG TCATCG PHGDH AK093306 CCTCAGATGAT 43GCAGGTCAAAACT  93 GCCTATCCA CTCAAAG PTTG1 BE904476 CAGCAAGCGAT 44AGCGGGCTTCTGT  94 GGCATAGT AATCTGA RRM2 AK123010 AATGCCACCGA 45GCCTCAGATTTCA  95 AGCCTC ACTCGT SFRP1 BC036503 TCGAACTGAAG 46CTGCTGAGAATCA  96 GCTATTTACGA AAGTGGGA G SLC39A6 NM_ GTCGAAGCCGC 47GGAACAAACTGCT  97 012319 AATTAGG CTGCCA TMEM45 AK098106 CAAACGTGTGT 48ACAGCTCTTTAGC  98 B TCTGGAGG ATTTGTGGA TYMS BQ56428 TGCCCTGTATG 49GGGACTATCAATG  99 ATGTCAGGA TTGGGTTCTC UBE2C BC032677 GTGAGGGGTGT 50CACACAGTTCACT 100 CAGCTCAGT GCTCCACA

“Gene expression” as used herein refers to the relative levels ofexpression and/or pattern of expression of a gene. The expression of agene may be measured at the level of DNA, cDNA, RNA, mRNA, orcombinations thereof “Gene expression profile” refers to the levels ofexpression of multiple different genes measured for the same sample. Anexpression profile can be derived from a biological sample collectedfrom a subject at one or more time points prior to, during, or followingdiagnosis, treatment, or therapy for breast cancer (or any combinationthereof), can be derived from a biological sample collected from asubject at one or more time points during which there is no treatment ortherapy for breast cancer (e.g., to monitor progression of disease or toassess development of disease in a subject at risk for breast cancer),or can be collected from a healthy subject. Gene expression profiles maybe measured in a sample, such as samples comprising a variety of celltypes, different tissues, different organs, or fluids (e.g., blood,urine, spinal fluid, sweat, saliva or serum) by various methodsincluding but not limited to microarray technologies and quantitativeand semi-quantitative RT-PCR techniques.

Clinical Variables

The PAM50 classification model described herein may be further combinedwith information on clinical variables to generate a continuous risk ofrelapse (ROR) predictor. As described herein, a number of clinical andprognostic breast cancer factors are known in the art and are used topredict treatment outcome and the likelihood of disease recurrence. Suchfactors include, for example, lymph node involvement, tumor size,histologic grade, estrogen and progesterone hormone receptor status,HER-2 levels, and tumor ploidy.

In one embodiment, risk of relapse (ROR) score is provided for a subjectdiagnosed with or suspected of having breast cancer. This score uses thePAM50 classification model in combination with clinical factors of lymphnode status (N) and tumor size (T). Assessment of clinical variables isbased on the American Joint Committee on Cancer (AJCC) standardizedsystem for breast cancer staging. In this system, primary tumor size iscategorized on a scale of 0-4 (T0: no evidence of primary tumor; T1: ≤2cm; T2: >2 cm-≤5 cm; T3: >5 cm; T4: tumor of any size with direct spreadto chest wall or skin). Lymph node status is classified as N0-N3 (N0:regional lymph nodes are free of metastasis; N1: metastasis to movable,same-side axillary lymph node(s); N2: metastasis to same-side lymphnode(s) fixed to one another or to other structures; N3: metastasis tosame-side lymph nodes beneath the breastbone). Methods of identifyingbreast cancer patients and staging the disease are well known and mayinclude manual examination, biopsy, review of patient's and/or familyhistory, and imaging techniques, such as mammography, magnetic resonanceimaging (MM), and positron emission tomography (PET).

Using the PAM50 classification methods of the present invention, theprognosis of a breast cancer patient can be determined independent of orin combination with assessment of these clinical factors. In someembodiments, combining the PAM50 breast cancer intrinsic subtypeclassification methods disclosed herein with evaluation of theseclinical factors may permit a more accurate risk assessment. The methodsof the invention may be further coupled with analysis of, for example,estrogen receptor (ER) and progesterone receptor (PgR) status, and/orHER-2 expression levels. Other factors, such as patient clinicalhistory, family history and menopausal status, may also be consideredwhen evaluating breast cancer prognosis via the methods of theinvention.

Sample Source

In one embodiment of the present invention, breast cancer subtype isassessed through the evaluation of expression patterns, or profiles, ofthe intrinsic genes listed in Table 1 in one or more subject samples.For the purpose of discussion, the term subject, or subject sample,refers to an individual regardless of health and/or disease status. Asubject can be a subject, a study participant, a control subject, ascreening subject, or any other class of individual from whom a sampleis obtained and assessed in the context of the invention. Accordingly, asubject can be diagnosed with breast cancer, can present with one ormore symptoms of breast cancer, or a predisposing factor, such as afamily (genetic) or medical history (medical) factor, for breast cancer,can be undergoing treatment or therapy for breast cancer, or the like.Alternatively, a subject can be healthy with respect to any of theaforementioned factors or criteria. It will be appreciated that the term“healthy” as used herein, is relative to breast cancer status, as theterm “healthy” cannot be defined to correspond to any absoluteevaluation or status. Thus, an individual defined as healthy withreference to any specified disease or disease criterion, can in fact bediagnosed with any other one or more diseases, or exhibit any other oneor more disease criterion, including one or more cancers other thanbreast cancer. However, the healthy controls are preferably free of anycancer.

In particular embodiments, the methods for predicting breast cancerintrinsic subtypes include collecting a biological sample comprising acancer cell or tissue, such as a breast tissue sample or a primarybreast tumor tissue sample. By “biological sample” is intended anysampling of cells, tissues, or bodily fluids in which expression of anintrinsic gene can be detected. Examples of such biological samplesinclude, but are not limited to, biopsies and smears. Bodily fluidsuseful in the present invention include blood, lymph, urine, saliva,nipple aspirates, gynecological fluids, or any other bodily secretion orderivative thereof. Blood can include whole blood, plasma, serum, or anyderivative of blood. In some embodiments, the biological sample includesbreast cells, particularly breast tissue from a biopsy, such as a breasttumor tissue sample. Biological samples may be obtained from a subjectby a variety of techniques including, for example, by scraping orswabbing an area, by using a needle to aspirate cells or bodily fluids,or by removing a tissue sample (i.e., biopsy). Methods for collectingvarious biological samples are well known in the art. In someembodiments, a breast tissue sample is obtained by, for example, fineneedle aspiration biopsy, core needle biopsy, or excisional biopsy.Fixative and staining solutions may be applied to the cells or tissuesfor preserving the specimen and for facilitating examination. Biologicalsamples, particularly breast tissue samples, may be transferred to aglass slide for viewing under magnification. In one embodiment, thebiological sample is a formalin-fixed, paraffin-embedded breast tissuesample, particularly a primary breast tumor sample. In variousembodiments, the tissue sample is obtained from a pathologist-guidedtissue core sample as described in Example 4.

Expression Profiling

In various embodiments, the present invention provides methods forclassifying, prognosticating, or monitoring breast cancer in subjects.In this embodiment, data obtained from analysis of intrinsic geneexpression is evaluated using one or more pattern recognitionalgorithms. Such analysis methods may be used to form a predictivemodel, which can be used to classify test data. For example, oneconvenient and particularly effective method of classification employsmultivariate statistical analysis modeling, first to form a model (a“predictive mathematical model”) using data (“modeling data”) fromsamples of known subtype (e.g., from subjects known to have a particularbreast cancer intrinsic subtype. LumA, LumB, Basal-like, HER2-enriched,or normal-like), and second to classify an unknown sample (e.g., “testsample”) according to subtype.

Pattern recognition methods have been used widely to characterize manydifferent types of problems ranging, for example, over linguistics,fingerprinting, chemistry and psychology. In the context of the methodsdescribed herein, pattern recognition is the use of multivariatestatistics, both parametric and non-parametric, to analyze data, andhence to classify samples and to predict the value of some dependentvariable based on a range of observed measurements. There are two mainapproaches. One set of methods is termed “unsupervised” and these simplyreduce data complexity in a rational way and also produce display plotswhich can be interpreted by the human eye. However, this type ofapproach may not be suitable for developing a clinical assay that can beused to classify samples derived from subjects independent of theinitial sample population used to train the prediction algorithm.

The other approach is termed “supervised” whereby a training set ofsamples with known class or outcome is used to produce a mathematicalmodel which is then evaluated with independent validation data sets.Here, a “training set” of intrinsic gene expression data is used toconstruct a statistical model that predicts correctly the “subtype” ofeach sample. This training set is then tested with independent data(referred to as a test or validation set) to determine the robustness ofthe computer-based model. These models are sometimes termed “expertsystems,” but may be based on a range of different mathematicalprocedures. Supervised methods can use a data set with reduceddimensionality (for example, the first few principal components), buttypically use unreduced data, with all dimensionality. In all cases themethods allow the quantitative description of the multivariateboundaries that characterize and separate each subtype in terms of itsintrinsic gene expression profile. It is also possible to obtainconfidence limits on any predictions, for example, a level ofprobability to be placed on the goodness of fit (see, for example,Kowalski et al., 1986). The robustness of the predictive models can alsobe checked using cross-validation, by leaving out selected samples fromthe analysis.

The PAM50 classification model described herein is based on the geneexpression profile for a plurality of subject samples using theintrinsic genes listed in Table 1. The plurality of samples includes asufficient number of samples derived from subjects belonging to eachsubtype class. By “sufficient samples” or “representative number” inthis context is intended a quantity of samples derived from each subtypethat is sufficient for building a classification model that can reliablydistinguish each subtype from all others in the group. A supervisedprediction algorithm is developed based on the profiles ofobjectively-selected prototype samples for “training” the algorithm. Thesamples are selected and subtyped using an expanded intrinsic gene setaccording to the methods disclosed in International Patent PublicationWO 2007/061876, which is herein incorporated by reference in itsentirety. Alternatively, the samples can be subtyped according to anyknown assay for classifying breast cancer subtypes. After stratifyingthe training samples according to subtype, a centroid-based predictionalgorithm is used to construct centroids based on the expression profileof the intrinsic gene set described in Table 1.

In one embodiment, the prediction algorithm is the nearest centroidmethodology related to that described in Narashiman and Chu (2002) PNAS99:6567-6572, which is herein incorporated by reference in its entirety.In the present invention, the method computes a standardized centroidfor each subtype. This centroid is the average gene expression for eachgene in each subtype (or “class”) divided by the within-class standarddeviation for that gene. Nearest centroid classification takes the geneexpression profile of a new sample, and compares it to each of theseclass centroids. Subtype prediction is done by calculating theSpearman's rank correlation of each test case to the five centroids, andassigning a sample to a subtype based on the nearest centroid.

Detection of Intrinsic Gene Expression

Any methods available in the art for detecting expression of theintrinsic genes listed in Table 1 are encompassed herein. By “detectingexpression” is intended determining the quantity or presence of an RNAtranscript or its expression product of an intrinsic gene.

Methods for detecting expression of the intrinsic genes of theinvention, that is, gene expression profiling, include methods based onhybridization analysis of polynucleotides, methods based on sequencingof polynucleotides, immunohistochemistry methods, and proteomics-basedmethods. The methods generally detect expression products (e.g., mRNA)of the intrinsic genes listed in Table 1. In preferred embodiments,PCR-based methods, such as reverse transcription PCR (RT-PCR) (Weis etal., TIG 8:263-64, 1992), and array-based methods such as microarray(Schena et al., Science 270:467-70, 1995) are used. By “microarray” isintended an ordered arrangement of hybridizable array elements, such as,for example, polynucleotide probes, on a substrate. The term “probe”refers to any molecule that is capable of selectively binding to aspecifically intended target biomolecule, for example, a nucleotidetranscript or a protein encoded by or corresponding to an intrinsicgene. Probes can be synthesized by one of skill in the art, or derivedfrom appropriate biological preparations. Probes may be specificallydesigned to be labeled. Examples of molecules that can be utilized asprobes include, but are not limited to, RNA, DNA, proteins, antibodies,and organic molecules.

Many expression detection methods use isolated RNA. The startingmaterial is typically total RNA isolated from a biological sample, suchas a tumor or tumor cell line, and corresponding normal tissue or cellline, respectively. If the source of RNA is a primary tumor, RNA (e.g.,mRNA) can be extracted, for example, from frozen or archivedparaffin-embedded and fixed (e.g., formalin-fixed) tissue samples (e.g.,pathologist-guided tissue core samples).

General methods for RNA extraction are well known in the art and aredisclosed in standard textbooks of molecular biology, including Ausubelet al., ed., Current Protocols in Molecular Biology, John Wiley & Sons,New York 1987-1999. Methods for RNA extraction from paraffin embeddedtissues are disclosed, for example, in Rupp and Locker (Lab Invest.56:A67, 1987) and De Andres et al. (Biotechniques 18:42-44, 1995). Inparticular, RNA isolation can be performed using a purification kit, abuffer set and protease from commercial manufacturers, such as Qiagen(Valencia, Calif.), according to the manufacturer's instructions. Forexample, total RNA from cells in culture can be isolated using QiagenRNeasy mini-columns. Other commercially available RNA isolation kitsinclude MASTERPURE® Complete DNA and RNA Purification Kit (Epicentre,Madison, Wis.) and Paraffin Block RNA Isolation Kit (Ambion, Austin,Tex.). Total RNA from tissue samples can be isolated, for example, usingRNA Stat-60 (Tel-Test, Friendswood, Tex.). RNA prepared from a tumor canbe isolated, for example, by cesium chloride density gradientcentrifugation. Additionally, large numbers of tissue samples canreadily be processed using techniques well known to those of skill inthe art, such as, for example, the single-step RNA isolation process ofChomczynski (U.S. Pat. No. 4,843,155).

Isolated RNA can be used in hybridization or amplification assays thatinclude, but are not limited to, PCR analyses and probe arrays. Onemethod for the detection of RNA levels involves contacting the isolatedRNA with a nucleic acid molecule (probe) that can hybridize to the mRNAencoded by the gene being detected. The nucleic acid probe can be, forexample, a full-length cDNA, or a portion thereof, such as anoligonucleotide of at least 7, 15, 30, 60, 100, 250, or 500 nucleotidesin length and sufficient to specifically hybridize under stringentconditions to an intrinsic gene of the present invention, or anyderivative DNA or RNA. Hybridization of an mRNA with the probe indicatesthat the intrinsic gene in question is being expressed.

In one embodiment, the mRNA is immobilized on a solid surface andcontacted with a probe, for example by running the isolated mRNA on anagarose gel and transferring the mRNA from the gel to a membrane, suchas nitrocellulose. In an alternative embodiment, the probes areimmobilized on a solid surface and the mRNA is contacted with theprobes, for example, in an Agilent gene chip array. A skilled artisancan readily adapt known mRNA detection methods for use in detecting thelevel of expression of the intrinsic genes of the present invention.

An alternative method for determining the level of intrinsic geneexpression product in a sample involves the process of nucleic acidamplification, for example, by RT-PCR (U.S. Pat. No. 4,683,202), ligasechain reaction (Barany, Proc. Natl. Acad. Sci. USA 88:189-93, 1991),self-sustained sequence replication (Guatelli et al., Proc. Natl. Acad.Sci. USA 87:1874-78, 1990), transcriptional amplification system (Kwohet al., Proc. Natl. Acad. Sci. USA 86:1173-77, 1989), Q-Beta Replicase(Lizardi et al., Bio/Technology 6:1197, 1988), rolling circlereplication (U.S. Pat. No. 5,854,033), or any other nucleic acidamplification method, followed by the detection of the amplifiedmolecules using techniques well known to those of skill in the art.These detection schemes are especially useful for the detection ofnucleic acid molecules if such molecules are present in very lownumbers.

In particular aspects of the invention, intrinsic gene expression isassessed by quantitative RT-PCR. Numerous different PCR or QPCRprotocols are known in the art and exemplified herein below and can bedirectly applied or adapted for use using the presently-describedcompositions for the detection and/or quantification of the intrinsicgenes listed in Table 1. Generally, in PCR, a target polynucleotidesequence is amplified by reaction with at least one oligonucleotideprimer or pair of oligonucleotide primers. The primer(s) hybridize to acomplementary region of the target nucleic acid and a DNA polymeraseextends the primer(s) to amplify the target sequence. Under conditionssufficient to provide polymerase-based nucleic acid amplificationproducts, a nucleic acid fragment of one size dominates the reactionproducts (the target polynucleotide sequence which is the amplificationproduct). The amplification cycle is repeated to increase theconcentration of the single target polynucleotide sequence. The reactioncan be performed in any thermocycler commonly used for PCR. However,preferred are cyclers with real-time fluorescence measurementcapabilities, for example, SMARTCYCLER® (Cepheid, Sunnyvale, Calif.),ABI PRISM 7700® (Applied Biosystems, Foster City, Calif.), ROTOR-GENE®(Corbett Research, Sydney, Australia), LIGHTCYCLER® (Roche DiagnosticsCorp, Indianapolis, Ind.), ICYCLER® (Biorad Laboratories, Hercules,Calif.) and MX4000® (Stratagene, La Jolla, Calif.).

Quantitative PCR (QPCR) (also referred as real-time PCR) is preferredunder some circumstances because it provides not only a quantitativemeasurement, but also reduced time and contamination. In some instances,the availability of full gene expression profiling techniques is limiteddue to requirements for fresh frozen tissue and specialized laboratoryequipment, making the routine use of such technologies difficult in aclinical setting. However, QPCR gene measurement can be applied tostandard formalin-fixed paraffin-embedded clinical tumor blocks, such asthose used in archival tissue banks and routine surgical pathologyspecimens (Cronin et al. (2007) Clin Chem 53:1084-91)[Mullins 2007][Paik 2004]. As used herein, “quantitative PCR (or “real time QPCR”)refers to the direct monitoring of the progress of PCR amplification asit is occurring without the need for repeated sampling of the reactionproducts. In quantitative PCR, the reaction products may be monitoredvia a signaling mechanism (e.g., fluorescence) as they are generated andare tracked after the signal rises above a background level but beforethe reaction reaches a plateau. The number of cycles required to achievea detectable or “threshold” level of fluorescence varies directly withthe concentration of amplifiable targets at the beginning of the PCRprocess, enabling a measure of signal intensity to provide a measure ofthe amount of target nucleic acid in a sample in real time.

In another embodiment of the invention, microarrays are used forexpression profiling. Microarrays are particularly well suited for thispurpose because of the reproducibility between different experiments.DNA microarrays provide one method for the simultaneous measurement ofthe expression levels of large numbers of genes. Each array consists ofa reproducible pattern of capture probes attached to a solid support.Labeled RNA or DNA is hybridized to complementary probes on the arrayand then detected by laser scanning. Hybridization intensities for eachprobe on the array are determined and converted to a quantitative valuerepresenting relative gene expression levels. See, for example, U.S.Pat. Nos. 6,040,138, 5,800,992 and 6,020,135, 6,033,860, and 6,344,316.High-density oligonucleotide arrays are particularly useful fordetermining the gene expression profile for a large number of RNAs in asample.

Techniques for the synthesis of these arrays using mechanical synthesismethods are described in, for example, U.S. Pat. No. 5,384,261. Althougha planar array surface is generally used, the array can be fabricated ona surface of virtually any shape or even a multiplicity of surfaces.Arrays can be nucleic acids (or peptides) on beads, gels, polymericsurfaces, fibers (such as fiber optics), glass, or any other appropriatesubstrate. See, for example, U.S. Pat. Nos. 5,770,358, 5,789,162,5,708,153, 6,040,193 and 5,800,992. Arrays can be packaged in such amanner as to allow for diagnostics or other manipulation of anall-inclusive device. See, for example, U.S. Pat. Nos. 5,856,174 and5,922,591.

In a specific embodiment of the microarray technique, PCR amplifiedinserts of cDNA clones are applied to a substrate in a dense array. Themicroarrayed genes, immobilized on the microchip, are suitable forhybridization under stringent conditions. Fluorescently labeled cDNAprobes can be generated through incorporation of fluorescent nucleotidesby reverse transcription of RNA extracted from tissues of interest.Labeled cDNA probes applied to the chip hybridize with specificity toeach spot of DNA on the array. After stringent washing to removenon-specifically bound probes, the chip is scanned by confocal lasermicroscopy or by another detection method, such as a CCD camera.Quantitation of hybridization of each arrayed element allows forassessment of corresponding mRNA abundance.

With dual color fluorescence, separately labeled cDNA probes generatedfrom two sources of RNA are hybridized pairwise to the array. Therelative abundance of the transcripts from the two sources correspondingto each specified gene is thus determined simultaneously. Theminiaturized scale of the hybridization affords a convenient and rapidevaluation of the expression pattern for large numbers of genes. Suchmethods have been shown to have the sensitivity required to detect raretranscripts, which are expressed at a few copies per cell, and toreproducibly detect at least approximately two-fold differences in theexpression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93:106-49,1996). Microarray analysis can be performed by commercially availableequipment, following manufacturer's protocols, such as by using theAffymetrix GenChip technology, or Agilent ink jet microarray technology.The development of microarray methods for large-scale analysis of geneexpression makes it possible to search systematically for molecularmarkers of cancer classification and outcome prediction in a variety oftumor types.

Data Processing

It is often useful to pre-process gene expression data, for example, byaddressing missing data, translation, scaling, normalization, weighting,etc. Multivariate projection methods, such as principal componentanalysis (PCA) and partial least squares analysis (PLS), are so-calledscaling sensitive methods. By using prior knowledge and experience aboutthe type of data studied, the quality of the data prior to multivariatemodeling can be enhanced by scaling and/or weighting. Adequate scalingand/or weighting can reveal important and interesting variation hiddenwithin the data, and therefore make subsequent multivariate modelingmore efficient. Scaling and weighting may be used to place the data inthe correct metric, based on knowledge and experience of the studiedsystem, and therefore reveal patterns already inherently present in thedata.

If possible, missing data, for example gaps in column values, should beavoided. However, if necessary, such missing data may replaced or“filled” with, for example, the mean value of a column (“mean fill”); arandom value (“random fill”); or a value based on a principal componentanalysis (“principal component fill”).

“Translation” of the descriptor coordinate axes can be useful. Examplesof such translation include normalization and mean centering.“Normalization” may be used to remove sample-to-sample variation. Formicroarray data, the process of normalization aims to remove systematicerrors by balancing the fluorescence intensities of the two labelingdyes. The dye bias can come from various sources including differencesin dye labeling efficiencies, heat and light sensitivities, as well asscanner settings for scanning two channels. Some commonly used methodsfor calculating normalization factor include: (i) global normalizationthat uses all genes on the array; (ii) housekeeping genes normalizationthat uses constantly expressed housekeeping/invariant genes; and (iii)internal controls normalization that uses known amount of exogenouscontrol genes added during hybridization (Quackenbush (2002) Nat. Genet.32 (Suppl.), 496-501). In one embodiment, the intrinsic genes disclosedherein can be normalized to control housekeeping genes. For example, thehousekeeping genes described in U.S. Patent Publication 2008/0032293,which is herein incorporated by reference in its entirety, can be usedfor normalization. Exemplary housekeeping genes include MRPL19, PSMC4,SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, and TFRC. It will be understood byone of skill in the art that the methods disclosed herein are not boundby normalization to any particular housekeeping genes, and that anysuitable housekeeping gene(s) known in the art can be used.

Many normalization approaches are possible, and they can often beapplied at any of several points in the analysis. In one embodiment,microarray data is normalized using the LOWESS method, which is a globallocally weighted scatterplot smoothing normalization function. Inanother embodiment, qPCR data is normalized to the geometric mean of setof multiple housekeeping genes.

“Mean centering” may also be used to simplify interpretation. Usually,for each descriptor, the average value of that descriptor for allsamples is subtracted. In this way, the mean of a descriptor coincideswith the origin, and all descriptors are “centered” at zero. In “unitvariance scaling,” data can be scaled to equal variance. Usually, thevalue of each descriptor is scaled by 1/StDev, where StDev is thestandard deviation for that descriptor for all samples. “Pareto scaling”is, in some sense, intermediate between mean centering and unit variancescaling. In pareto scaling, the value of each descriptor is scaled by1/sqrt(StDev), where StDev is the standard deviation for that descriptorfor all samples. In this way, each descriptor has a variance numericallyequal to its initial standard deviation. The pareto scaling may beperformed, for example, on raw data or mean centered data.

“Logarithmic scaling” may be used to assist interpretation when datahave a positive skew and/or when data spans a large range, e.g., severalorders of magnitude. Usually, for each descriptor, the value is replacedby the logarithm of that value. In “equal range scaling,” eachdescriptor is divided by the range of that descriptor for all samples.In this way, all descriptors have the same range, that is, 1. However,this method is sensitive to presence of outlier points. In“autoscaling,” each data vector is mean centered and unit variancescaled. This technique is a very useful because each descriptor is thenweighted equally, and large and small values are treated with equalemphasis. This can be important for genes expressed at very low, butstill detectable, levels.

In one embodiment, data is collected for one or more test samples andclassified using the PAM50 classification model described herein. Whencomparing data from multiple analyses (e.g., comparing expressionprofiles for one or more test samples to the centroids constructed fromsamples collected and analyzed in an independent study), it will benecessary to normalize data across these data sets. In one embodiment,Distance Weighted Discrimination (DWD) is used to combine these datasets together (Benito et al. (2004) Bioinformatics 20(1):105-114,incorporated by reference herein in its entirety). DWD is a multivariateanalysis tool that is able to identify systematic biases present inseparate data sets and then make a global adjustment to compensate forthese biases; in essence, each separate data set is a multi-dimensionalcloud of data points, and DWD takes two points clouds and shifts onesuch that it more optimally overlaps the other.

The methods described herein may be implemented and/or the resultsrecorded using any device capable of implementing the methods and/orrecording the results. Examples of devices that may be used include butare not limited to electronic computational devices, including computersof all types. When the methods described herein are implemented and/orrecorded in a computer, the computer program that may be used toconfigure the computer to carry out the steps of the methods may becontained in any computer readable medium capable of containing thecomputer program. Examples of computer readable medium that may be usedinclude but are not limited to diskettes, CD-ROMs, DVDs, ROM, RAM, andother memory and computer storage devices. The computer program that maybe used to configure the computer to carry out the steps of the methodsand/or record the results may also be provided over an electronicnetwork, for example, over the internet, an intranet, or other network.

Calculation of Risk of Relapse

Provided herein are methods for predicting breast cancer outcome withinthe context of the intrinsic subtype and optionally other clinicalvariables. Outcome may refer to overall or disease-specific survival,event-free survival, or outcome in response to a particular treatment ortherapy. In particular, the methods may be used to predict thelikelihood of long-term, disease-free survival. “Predicting thelikelihood of survival of a breast cancer patient” is intended to assessthe risk that a patient will die as a result of the underlying breastcancer. “Long-term, disease-free survival” is intended to mean that thepatient does not die from or suffer a recurrence of the underlyingbreast cancer within a period of at least five years, or at least ten ormore years, following initial diagnosis or treatment.

In one embodiment, outcome is predicted based on classification of asubject according to subtype. This classification is based on expressionprofiling using the list of intrinsic genes listed in Table 1. Asdiscussed in Example 1, tumor subtype according to the PAM50 model wasmore indicative of response to chemotherapy than standard clinicalmarker classification. Tumors classified as HER2+ using clinical markersbut not HER2-enriched using the PAM50 model had a lower pathologicalcomplete response (pCR) to a regimen of paclitaxel, 5-fluorouracil,adriamycin, and cyclophosphamide (T/FAC) than tumors classified as HER2+clinically and belonging to the HER2-enriched expression subtype.Similarly, Basal-like tumors that were not clinically scored astriple-negative (ER−, PgR− and HER2−) had a higher pCR compared totriple-negative tumors that were not Basal-like by PAM50. Thus, thePAM50 model can be used to more accurately predict response tochemotherapy than standard clinical markers.

In addition to providing a subtype assignment, the PAM50 bioinformaticsmodel provides a measurement of the similarity of a test sample to allfour subtypes which is translated into a Risk Of Relapse (ROR) scorethat can be used in any patient population regardless of disease statusand treatment options. The intrinsic subtypes and ROR also have value inthe prediction of pathological complete response in women treated with,for example, neoadjuvant taxane and anthracycline chemotherapy [Rouzier2005]. Thus, in various embodiments of the present invention, a risk ofrelapse (ROR) model is used to predict outcome. Using these risk models,subjects can be stratified into low, medium, and high risk of relapsegroups. Calculation of ROR can provide prognostic information to guidetreatment decisions and/or monitor response to therapy.

In some embodiments described herein, the prognostic performance of thePAM50-defined intrinsic subtypes and/or other clinical parameters isassessed utilizing a Cox Proportional Hazards Model Analysis, which is aregression method for survival data that provides an estimate of thehazard ratio and its confidence interval. The Cox model is awell-recognized statistical technique for exploring the relationshipbetween the survival of a patient and particular variables. Thisstatistical method permits estimation of the hazard (i.e., risk) ofindividuals given their prognostic variables (e.g., intrinsic geneexpression profile with or without additional clinical factors, asdescribed herein). The “hazard ratio” is the risk of death at any giventime point for patients displaying particular prognostic variables. Seegenerally Spruance et al., Antimicrob. Agents & Chemo. 48:2787-92, 2004.

The PAM50 classification model described herein can be trained for riskof relapse using subtype distances (or correlations) alone, or usingsubtype distances with clinical variables as discussed supra. In oneembodiment, the risk score for a test sample is calculated usingintrinsic subtype distances alone using the following equation:ROR=0.05*Basal+0.11*Her2+−0.25*LumA+0.07*LumB+−0.11*Normal, where thevariables “Basal,” “Her2,” “LumA,” “LumB,” and “Normal” are thedistances to the centroid for each respective classifier when theexpression profile from a test sample is compared to centroidsconstructed using the gene expression data deposited with the GeneExpression Omnibus (GEO) as accession number GSE2845. It is alsopossible that other data sets could be used to derive similar Cox Modelcoefficients. When using the intrinsic gene list set forth in Table 1 todevelop a prediction model from a sample set other than the samples usedto derive the dataset deposited as GSE2845, the methods described inExample 1 or Example 3 can be used to construct a formula forcalculating the risk of relapse from this alternate sample set.

Risk score can also be calculated using a combination of breast cancersubtype and the clinical variables tumor size (T) and lymph nodes status(N) using the following equation: ROR(full)=0.05*Basal+0.1*Her2+−0.19*LumA+0.05*LumB+−0.09*Normal+0.16*T+0.08*−N,again when comparing test expression profiles to centroids constructedusing the gene expression data deposited with GEO as accession numberGSE2845.

In yet another embodiment, risk score for a test sample is calculatedusing intrinsic subtype distances alone using the following equation:ROR-S=0.05*Basal+0.12*Her2+−0.34*LumA+0.0.23*LumB,

where the variables “Basal,” “Her2,” “LumA,” and “LumB” are as describedsupra and the test expression profiles are compared to centroidsconstructed using the gene expression data deposited with GEO asaccession number GSE2845.

In yet another embodiment, risk score can also be calculated using acombination of breast cancer subtype and the clinical variable tumorsize (T) using the following equation (where the variables are asdescribed supra):ROR-C=0.05*Basal+0.11*Her2+−0.23*LumA+0.09*LumB+0.17*T.

Prediction of Response to Therapy

Breast cancer is managed by several alternative strategies that mayinclude, for example, surgery, radiation therapy, hormone therapy,chemotherapy, or some combination thereof. As is known in the art,treatment decisions for individual breast cancer patients can be basedon endocrine responsiveness of the tumor, menopausal status of thepatient, the location and number of patient lymph nodes involved,estrogen and progesterone receptor status of the tumor, size of theprimary tumor, patient age, and stage of the disease at diagnosis.Analysis of a variety of clinical factors and clinical trials has led tothe development of recommendations and treatment guidelines forearly-stage breast cancer by the International Consensus Panel of theSt. Gallen Conference (2005). See, Goldhirsch et al., Annals Oncol.16:1569-83, 2005. The guidelines recommend that patients be offeredchemotherapy for endocrine non-responsive disease; endocrine therapy asthe primary therapy for endocrine responsive disease, addingchemotherapy for some intermediate- and all high-risk groups in thiscategory; and both chemotherapy and endocrine therapy for all patientsin the uncertain endocrine response category except those in thelow-risk group.

Stratification of patients according to risk of relapse using the PAM50model and risk score disclosed herein provides an additional oralternative treatment decision-making factor. The methods compriseevaluating risk of relapse using the PAM50 classification modeloptionally in combination with one or more clinical variables, such asnode status, tumor size, and ER status. The risk score can be used toguide treatment decisions. For example, a subject having a low riskscore may not benefit from certain types of therapy, whereas a subjecthaving a high risk score may be indicated for a more aggressive therapy.

The methods of the invention find particular use in choosing appropriatetreatment for early-stage breast cancer patients. The majority of breastcancer patients diagnosed at an early-stage of the disease enjoylong-term survival following surgery and/or radiation therapy withoutfurther adjuvant therapy. However, a significant percentage(approximately 20%) of these patients will suffer disease recurrence ordeath, leading to clinical recommendations that some or all early-stagebreast cancer patients should receive adjuvant therapy. The methods ofthe present invention find use in identifying this high-risk, poorprognosis population of early-stage breast cancer patients and therebydetermining which patients would benefit from continued and/or moreaggressive therapy and close monitoring following treatment. Forexample, early-stage breast cancer patients assessed as having a highrisk score by the methods disclosed herein may be selected for moreaggressive adjuvant therapy, such as chemotherapy, following surgeryand/or radiation treatment. In particular embodiments, the methods ofthe present invention may be used in conjunction with the treatmentguidelines established by the St. Gallen Conference to permit physiciansto make more informed breast cancer treatment decisions.

In various embodiments, the PAM50 classification model providesinformation about breast cancer subtypes that cannot be obtained usingstandard clinical assays such as immunohistochemistry or otherhistological analyses. For example, subjects scored as estrogen receptor(ER)-positive and/or progesterone-receptor (PR)-positive would beindicated under conventional guidelines for endocrine therapy. Asdiscussed in Example 2, the model disclosed herein is capable ofidentifying a subset of these ER+/PgR+ cases that are classified asBasal-like, which may indicate the need for more aggressive therapy thatwould not have been indicated based on ER or PgR status alone.

Thus, the methods disclosed herein also find use in predicting theresponse of a breast cancer patient to a selected treatment. “Predictingthe response of a breast cancer patient to a selected treatment” isintended to mean assessing the likelihood that a patient will experiencea positive or negative outcome with a particular treatment. As usedherein, “indicative of a positive treatment outcome” refers to anincreased likelihood that the patient will experience beneficial resultsfrom the selected treatment (e.g., complete or partial remission,reduced tumor size, etc.). “Indicative of a negative treatment outcome”is intended to mean an increased likelihood that the patient will notbenefit from the selected treatment with respect to the progression ofthe underlying breast cancer.

In some embodiments, the relevant time for assessing prognosis ordisease-free survival time begins with the surgical removal of the tumoror suppression, mitigation, or inhibition of tumor growth. In anotherembodiment, the PAM50-based risk score is calculated based on a sampleobtained after initiation of neoadjuvant therapy such as endocrinetherapy. The sample may be taken at any time following initiation oftherapy, but is preferably obtained after about one month so thatneoadjuvant therapy can be switched to chemotherapy in unresponsivepatients. It has been shown that a subset of tumors indicated forendocrine treatment before surgery is non-responsive to this therapy.The model provided herein can be used to identify aggressive tumors thatare likely to be refractory to endocrine therapy, even when tumors arepositive for estrogen and/or progesterone receptors. In this embodiment,a proliferation-weighted PAM50 risk score is obtained according thefollowing equation:RSp=(−0.0129*Basal)+(0.106*Her2)+(−0.112*LumA)+(0.039*LumB)+(−0.069*Normal)+(0.272*Prolif),where the proliferation score (“prolif”) is assigned as the meanmeasurement of the following genes (after normalization): CCNB1, UBE2C,BIRCS, KNTC2, CDC20, PTTG1, RRM2, MKI67, TYMS, CEP55, and CDCA1. Allother variables are the same as the RS equations described infra. Asdiscussed in Example 2, assessment of risk score after initiation oftherapy is more predictive of outcome to treatment, at least in apopulation of ER+ patients undergoing neoadjuvant endocrine therapy.

Kits

The present invention also provides kits useful for classifying breastcancer intrinsic subtypes and/or providing prognostic information. Thesekits comprise a set of capture probes and/or primers specific for theintrinsic genes listed in Table 1, as well as reagents sufficient tofacilitate detection and/or quantitation of the intrinsic geneexpression product. The kit may further comprise a computer readablemedium.

In one embodiment of the present invention, the capture probes areimmobilized on an array. By “array” is intended a solid support or asubstrate with peptide or nucleic acid probes attached to the support orsubstrate. Arrays typically comprise a plurality of different captureprobes that are coupled to a surface of a substrate in different, knownlocations. The arrays of the invention comprise a substrate having aplurality of capture probes that can specifically bind an intrinsic geneexpression product. The number of capture probes on the substrate varieswith the purpose for which the array is intended. The arrays may below-density arrays or high-density arrays and may contain 4 or more, 8or more, 12 or more, 16 or more, 32 or more addresses, but willminimally comprise capture probes for the 50 intrinsic genes listed inTable 1.

Techniques for the synthesis of these arrays using mechanical synthesismethods are described in, e.g., U.S. Pat. No. 5,384,261, incorporatedherein by reference in its entirety for all purposes. The array may befabricated on a surface of virtually any shape or even a multiplicity ofsurfaces. Arrays may be probes (e.g., nucleic-acid binding probes) onbeads, gels, polymeric surfaces, fibers such as fiber optics, glass orany other appropriate substrate, see U.S. Pat. Nos. 5,770,358,5,789,162, 5,708,153, 6,040,193 and 5,800,992, each of which is herebyincorporated in its entirety for all purposes. Arrays may be packaged insuch a manner as to allow for diagnostics or other manipulation on thedevice. See, for example, U.S. Pat. Nos. 5,856,174 and 5,922,591 hereinincorporated by reference.

In another embodiment, the kit comprises a set of oligonucleotideprimers sufficient for the detection and/or quantitation of each of theintrinsic genes listed in Table 1. The oligonucleotide primers may beprovided in a lyophilized or reconstituted form, or may be provided as aset of nucleotide sequences. In one embodiment, the primers are providedin a microplate format, where each primer set occupies a well (ormultiple wells, as in the case of replicates) in the microplate. Themicroplate may further comprise primers sufficient for the detection ofone or more housekeeping genes as discussed infra. The kit may furthercomprise reagents and instructions sufficient for the amplification ofexpression products from the genes listed in Table 1.

In order to facilitate ready access, e.g., for comparison, review,recovery, and/or modification, the molecular signatures/expressionprofiles are typically recorded in a database. Most typically, thedatabase is a relational database accessible by a computational device,although other formats, e.g., manually accessible indexed files ofexpression profiles as photographs, analogue or digital imagingreadouts, spreadsheets, etc. can be used. Regardless of whether theexpression patterns initially recorded are analog or digital in nature,the expression patterns, expression profiles (collective expressionpatterns), and molecular signatures (correlated expression patterns) arestored digitally and accessed via a database. Typically, the database iscompiled and maintained at a central facility, with access beingavailable locally and/or remotely.

The article “a” and “an” are used herein to refer to one or more thanone (i.e., to at least one) of the grammatical object of the article. Byway of example, “an element” means one or more element.

Throughout the specification the word “comprising,” or variations suchas “comprises” or “comprising,” will be understood to imply theinclusion of a stated element, integer or step, or group of elements,integers or steps, but not the exclusion of any other element, integeror step, or group of elements, integers or steps.

The following examples are offered by way of illustration and not by wayof limitation:

EXPERIMENTAL Example 1 Methods Samples and Clinical Data:

Patient cohorts for training and test sets consisted of samples withdata already in the public domain (Loi et al. (2007) J. Clin. Oncol.25:1239-1246; va de Vijver et al. (2002) N Engl J Med 247:1999-2009;Wang et al (2005) Lancet 365:671-679; Ishvina et al. (2006) Cancer Res66:10292-10301; and Hess et al (2006) J Clin Oncol 24:4236-4244, each ofwhich is incorporated by reference in its entirety) and fresh frozen andformalin-fixed paraffin-embedded (FFPE) tissues collected underinstitutional review board-approved protocols at the respectiveinstitutions.

A training set of 189 breast tumor samples and 29 normal samples wasprocured as fresh frozen and FFPE tissues under approved IRB protocolsat the University of North Carolina at Chapel Hill, The University ofUtah, Thomas Jefferson University, and Washington University. Thetraining set, which was gene expression profiled by microarray andqRT-PCR, had a median follow-up of 49 months and representsheterogeneously treated patients in accordance with the standard of caredictated by their stage, ER and HER2 status. A test set of 279 breastcancers with long-term, disease-specific survival was gene expressionprofiled from FFPE by qRT-PCR. The clinical data for the training andtest set assayed by qRT-PCR are provided in Tables 2 and 3.

Nucleic Acid Extraction:

Total RNA was purified from fresh frozen samples for microarray usingthe Qiagen RNeasy Midi Kit according to the manufacturer's protocol(Qiagen, Valencia Calif.). The integrity of the RNA was determined usingan Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, Calif.).The High Pure RNA Paraffin Kit (Roche Applied Science, Indianapolis,Ind.) was used to extract RNA from FFPE tissues (2×10 micron or 1.5 mmpunches) for qRT-PCR. Contaminating DNA was removed using Turbo DNase(Ambion, Austin, Tex.). The yield of total RNA was assessed using theNanoprop ND-1000 Spectrophotometer (Nanoprop Technologies, Inc.,Rockland, Del.).

Reverse Transcription and Real-Time Quantitative PCR:

First-strand cDNA was synthesized from 1.2 μg total RNA usingSuperscript III reverse transcriptase (1st Strand Kit; Invitrogen,Carlsbad, Calif.) and a mixture of random hexamers and gene specificprimers. The reaction was held at 55° C. for 60 minutes and then 70° C.for 15 minutes. The cDNA was washed on a QIAquick PCR purificationcolumn (Qiagen Inc., Valencia, Calif.) and stored at −80° C. in 25 mMTris, 1 mM EDTA until further use. Each 54, PCR reaction included 1.25ng (0.625 ng/μL) cDNA from samples of interest or 10 ng (5 ng/μL) forreference, 2 pmol of both upstream and downstream primers, and 2×LightCycler 480 SYBR Green I Master Mix (Roche Applied Science,Indianapolis, Ind.). Each run contained a single gene profiled induplicate for test samples, reference sample, and negative control. Thereference sample cDNA was comprised of an equal contribution of HumanReference Total RNA (Stratagene, La Jolla, Calif.) and the breast celllines MCF7, ME16C, and SKBR3. PCR amplification was performed with theLightCycler 480 (Roche Applied Science, Indianapolis, Ind.) using aninitial denaturation step (95° C., 8 minutes) followed by 45 cycles ofdenaturation (95° C., 4 seconds), annealing (56° C., 6 seconds with 2.5°C./s transition), and extension (72° C., 6 seconds with 2° C./sectransition). Fluorescence (530 nm) from the dsDNA dye SYBR Green I wasacquired each cycle after the extension step. The specificity of the PCRwas determined by post-amplification melting curve analysis—samples werecooled to 65° C. and slowly heated at 2° C./s to 99° C. whilecontinuously monitoring fluorescence (10 acquisitions/1° C.). Therelative copy number for each gene was determined from a within runcalibrator set at 10 ng and using a PCR efficiency of 1.9. Each of thePAM50 classifier genes was normalized to the geometric mean of 5housekeepers.

Microarray:

Total RNA isolation, labeling and hybridizations on Agilent human 1Av2microarrays or custom designed Agilent human 22 k arrays were performedusing the protocol described in Hu et al (6). All microarray data havebeen deposited into the GEO under the accession number of GSE10886.Sources for all microarray training and test data sets are given inTable 4.

Pre-Processing of Microarray Data:

Microarray data for the training set (189 samples) were extracted fromthe University of North Carolina (UNC) microarray database. Raw signalintensities from both channels were lowess normalized by chip and probeswere excluded from data analysis if they did not have signal intensityof at least 30 in both channels for at least 70% of the experiments. Thenormalized data for this set have been placed on GEO (GSE10886). Thetraining set was median-centered and gene symbols were assigned usingthe manufacturer provided annotation. Duplicate gene symbols werecollapsed by averaging within each sample.

Normalized data for all test sets were downloaded from GEO (GSE2845,GSE6532, GSE4922, GSE2034, GSE10886) or the publicly-available datafound at the world wide web (www) at bioinformatics.mdanderson [dot]org/pubdata (see Table 5). All intensity measures (ratios for the NKIdata) were log-transformed. Prior to nearest centroid calculation, theHess et al. (see the world wide web (www) at bioinformatics.mdanderson[dot] org/pubdata), van de Vijver et al. (GSE2845), and Wang et al.(GSE2034) datasets were median centered to minimize platform effects.Adjustment in this way assumes a relatively similar sampling of thepopulation as the training set. The Loi et al. (GSE6532) and Ivshina etal. (GSE4922) datasets were heavily enriched for ER+ samples relative tothe training set, thus the underlying assumption may be violated forthese sets. In these two instances the genes in the training set werecentered to the median of the ER+ samples (as opposed to the medianacross all samples). As with the training set, gene symbols wereassigned using the manufacturer provided annotation, and duplicate genesymbols were collapsed by averaging within each sample.

Identification of Prototypical Intrinsic Subtype Samples and Genes:

An expanded “intrinsic” gene set, comprised primarily of genes found in4 previous studies (1, 6, 9, 11), was initially used to identifyprototypical tumor samples. The Normal-like class was represented usingtrue “normals” from reduction mammoplasty or grossly uninvolved tissue.189 breast tumors across 1906 “intrinsic” genes were analyzed byhierarchical clustering (median centered by feature/gene, Pearsoncorrelation, average linkage) (12) and the sample dendrogram wasanalyzed using “SigClust” (13). The SigClust algorithm statisticallyidentifies significant/unique groups by testing the null hypothesis thata group of samples is from a single cluster, where a cluster ischaracterized as a multivariate normal distribution. SigClust was run ateach node of the dendrogram beginning at the root and stopping when thetest was no longer significant (p>0.001).

Gene Set Reduction Using Prototype Samples and qRT-PCR:

122 breast cancers from 189 individuals profiled by qRT-PCR andmicroarray had prototypical profiles as determined by SigClust (Table2). A minimized gene set was derived from these prototypical samplesusing the qRT-PCR data for 161 genes that passed FFPE performancecriteria established in Mullins et al (14). Several minimization methodswere employed including top “N” t-test statistics for each group (15),top cluster index scores (16), and the remaining genes after ‘shrinkage’of modified t-test statistics (17). Cross-validation (random 10% leftout in each of 50 cycles) was used to assess the robustness of theminimized gene sets. The “N” t-test method was selected due to havingthe lowest CV error.

Sample Subtype Prediction:

Minimized gene sets were compared for reproducibility of classificationacross 3 centroid-based prediction methods: Prediction Analysis ofMicroarray (PAM) (17), a simple nearest centroid (6), and Classificationof Nearest Centroid (ClaNC) (18). Subtype prediction was done bycalculating the Spearman's rank correlation of each test case to fivecentroids (LumA, LumB, HER2-enriched, Basal-like, and Normal-like) andclass was assigned based upon the nearest centroid. Centroids wereconstructed as described for the PAM algorithm (17) using the dataprovided in GSE10886; however, no “shrinkage” was used and theSpearman's rank correlation was used for the distance measure. Thismethod was selected as the classifier because of its reproducibility ofsubtype predictions from large and minimized gene sets. The final50-gene classifier (henceforth called PAM50) was used to make subtypepredictions onto 6 microarray datasets and 1 qRT-PCR dataset (Table 4).The Hess et al dataset (19) does not have outcome data and is evaluatedbased on clinical markers, subtypes, and neo-adjuvant response.

Prognosis Using Clinical and Molecular Subtype Data:

The prognostic significance of the intrinsic subtype classification wasassessed along with standard clinical variables (tumor size (T), nodestatus (N), and ER status) using univariate and multivariate analyseswith time to relapse (i.e., any event) as the endpoint. Likelihood ratiotests were performed to compare models of available clinical data,subtype data, and combined clinical and molecular variables. Categoricalsurvival analyses were performed using a log rank test and visualizedwith Kaplan-Meier plots.

Developing Risk Models with Clinical and Molecular Data:

Models were trained for risk of relapse (ROR) predictions using subtypealone, and subtype with clinical information. In both cases, amultivariate Cox model using Ridge regression was fit to the untreatedsubset of the NKI295 cohort (20). A risk score was assigned to each testcase using correlation to the subtype alone (ROR; model 1) or using afull model with subtype correlation and two clinical variables (ROR(full); model 2):

ROR=0.05*Basal+0.11*Her2+−0.25*LumA+0.07*LumB+−0.11*Normal  (1)

ROR(full)=0.05*Basal+0.1*Her2+−0.19*LumA+0.05*LumB+−0.09*Normal+0.16*T+0−0.08*N  (2)

The sum of the coefficients from the Cox model is the “risk of relapse”score for each patient. In order to classify samples into specific riskgroups, thresholds were chosen from the NKI training set that requiredno LumA sample to be in the high risk group and no Basal-like sample tobe in the low risk group. Thresholds were determined from the trainingset and remained unchanged when evaluating test cases. Predictions forthe subtype only and combined models were compared using the C Index(see the world wide web (www) at lib.stat.cmu [dot]edu/S/Harrell/Design.html). SiZer analysis was performed to characterizethe relationship between the ROR score and relapse free survival (21).The 95% confidence intervals for the ROR score are local versions ofbinomial confidence intervals, with the local sample size computed froma Gaussian kernel density estimator, based on the Sheather-Jones choiceof window width (22).

Results Creating a New Subtype Model Based Upon Prototypical Samples andGenes:

There have been numerous studies that have analyzed interactions betweenbreast cancer intrinsic subtypes and prognosis (1, 6, 9), geneticalterations (23), and drug response (24). The purpose of the methodsdescribed here was to standardize and validate a classification for theintrinsic subtypes for clinical and research purposes. “SigClust”objectively identified five intrinsic breast subtypes from clusteredmicroarray data. These prototypes were then used to derive a minimal50-gene set (PAM50). Finally, the best classification method wasselected and used with the PAM50 to predict subtypes on multiple testsets from microarray and qRT-PCR data. Of the 5 microarray studies withoutcome data (Table 4), the UNC cohort had significantly worse outcomesthan the others. Subtype predictions onto a combined microarray test setshowed prognostic significance across all patients, in patients givenendocrine treatment alone, and in node negative patients receiving nosystemic adjuvant therapy (FIGS. 1A to 1C).

Molecular and clinical predictors of survival were assessed inunivariate and multivariate analyses on 1451 patients (Table 5). Inunivariate analysis, the LumA, LumB, and HER2-enriched subtypes were allfound to be significant, as were the clinical variables ER, T, and N.The LumA and HER2-enriched subtypes and the clinical variables were alsosignificant in multivariate analyses, suggesting that the mostcomprehensive model should include subtype and clinical information.Testing this hypothesis revealed that the combined model accounts forsignificantly more variation in survival than either the subtype orclinical variables alone (p<0.0001 for both tests).

Distribution of Biological Subtypes Across ER Positive and ER-NegativeTumors:

Of all ER-positive tumors in the combined microarray test set, 73% wereLuminal (A and B), 10% were HER2-enriched, and 5% were Basal-like (Table6). Conversely when ER-negative tumors were considered, approximately13% were Luminal (A and B), 31% were HER2-enriched and 48% wereBasal-like. Tumors identified as the Normal-like subtype were dividedalmost equally between ER-positive (11%) and ER-negative (8%) tumors.Therefore, while subtype representation markedly changed in distributiondepending on ER-status, all subtypes were represented in bothER-positive and ER-negative categories. Outcome plots for the subtypesin ER-positive cases alone were significant for relapse free survivaland followed the same trends as seen when considering all invasivebreast disease.

Subtypes and Response to Neoadjuvant T/FAC Treatment:

The Hess et al. study that performed microarray on tumors from patientsgiven a regimen of paclitaxel, 5-fluorouracil, adriamycin, andcyclophosphamide (T/FAC) (19) allowed investigation of the relationshipbetween the PAM50 subtypes, clinical markers, and how each relates topathological complete response (pCR). For HER2 status, 64% of tumorsthat were HER2-positive by clinical assay (FISH+ and/or IHC 3+, referredto as HER2+clin) were classified into the HER2-enriched expressionsubtype, with the rest of the HER2+clin mostly associated with theLuminal subtypes. Tumors that were HER2+clin but not of theHER2-enriched expression subtype had a low pCR rate (16%) versus thosethat were HER2+clin and HER2-enriched expression subtype (52%).

Another relevant clinical distinction is the classification of“triple-negative” tumors (ER−, PgR− and HER2−), of which 65% were calledBasal-like by the PAM50, with the remainder being called HER2-enriched(15%), LumA (4%), LumB (4%), and Normal-like (12%). The PAM50classification of Basal-like appears superior to the clinicaltriple-negative with respect to pCR rate in that Basal-like tumors thatwere not scored as triple-negative had a 50% pCR compared totriple-negative tumors that were not Basal-like by PAM50 (22% pCR, Table7).

Risk Prediction Based on Biological Subtype:

A supervised risk classifier was developed to predict outcomes withinthe context of the intrinsic subtypes and clinical variables. Anuntreated cohort was selected from the NKI microarray dataset to trainthe risk of relapse (ROR) model and select cut-offs. Two Cox models (onebased upon subtype alone and another based upon subtype, tumor size, andnode status) were validated using the combined microarray test set.Excluding clinical variables, the subtype only model performed well atstratifying patients into low, medium, and high risk of relapse groups(c-index=0.65 [0.61−0.69]); however, the full model (subtype, tumorsize, node status) performed better (c-index=0.70 [0.66−0.74]), and, inpractice, stage is a parameter that needs to be accounted for (FIGS. 2Ato 2D). FIGS. 3A and 3B show the probability of relapse-free survival at5 years plotted as a continuous linear scale using the full model.

The PAM50 classifier, assayed by qRT-PCR, was applied to aheterogeneously treated cohort archived between 1976 and 1995. Thesubtype classifications followed the same survival trends as seen in themicroarray data and the ROR score was significant for long-term relapsepredictions. This old age sample set was also scored for standardclinical markers (ER and HER2) by immunohistochemistry (IHC) andcompared to the gene expression-based test. Analysis of ESR1 and ERBB2by gene expression showed high sensitivity and specificity as comparedto the IHC assay.

Discussion

The PAM50 classifier was developed using a statistically derived geneand sample set and was validated across multiple cohorts and platformswith the intent of delivering a clinical diagnostic test for theintrinsic subtypes of breast cancer. The large and diverse test setsallowed evaluation of the performance of the assay at a population leveland in relation to standard molecular markers. An important finding fromthese analyses is that all of the intrinsic subtypes are present withinboth clinically defined ER-positive and ER-negative tumor subsets, withthe subtype designations in the ER-positive patients showing prognosticsignificance. Thus, the molecular subtypes are not simply another methodof classification based upon ER status.

There were also other important findings concerning individual subtypes.For example, some of the tumors classified into the HER2-enrichedexpression subtype were not HER2+clin, suggesting the presence of anER-negative non-Basal subtype that is not driven by HER2 geneamplification. It was also found that about 10% of breast cancers wereclassified as Normal-like and can be either ER-positive or ER-negativeand have an intermediate prognosis. Since these tumors were predicted bytraining on normal breast tissue, the Normal-like class may be anartifact of having a high percentage of normal “contamination” in thetumor specimen. Other possibilities are that these are slow growingBasal-like tumors that lack high expression of the proliferation genes,or are a potential new subtype that has been referred to as claudin-lowtumors (25). Detailed histological, immunohistochemical, and additionalgene expression analyses of these cases are needed to resolve theseissues.

Discrepancies between subtype and standard molecular markers haveimportant therapeutic implications. For instance, a patient with aBasal-like subtype tumor that was scored ER or PgR-positive would likelybe treated by endocrine therapy and would not be eligible for protocolsthat aim to develop Basal-like specific therapies (e.g., platinumcontaining regimens). These analyses of the Hess et al. dataset (19)showed that no patient with the LumA subtype had a pCR when administeredan aggressive neoadjuvant regimen whereas the pCR rate of the Basal-liketumors was 59%. Furthermore, there has been debate about whether thetriple-negative (ER−, PR−, HER2−) phenotype is the same as theBasal-like expression subtype 26. A recent tissue microarray study of3744 tumors confirmed the poor prognosis of triple-negative cases, butalso revealed that tumors lacking all markers did not behave the same asthose that were positive for one or two Basal-like markers (i.e., CK5/6or HER1) (27). In agreement with the idea that the Basal-like diagnosisshould be made independent of clinical ER and PgR status, a highertherapeutic response to T/FAC was found in those subjects identified asBasal-like but non-triple negative (50%) versus those identified astriple-negative but not Basal-like (22%). This suggests that theBasal-like subtype designation may ultimately prove superior to thetriple-negative definition in identifying tumors with a high degree ofchemotherapy sensitivity.

Providing an absolute subtype classification is somewhat artificial astumors do not exist as discrete biological entities. Classification oftumors into low-medium-high risk groups based upon distance to eachsubtype centroid (i.e., the ROR model) was an attempt to deal with thisissue and yielded significant survival segregation. This was true whencombining all test cases, or after stratification into cohorts givenendocrine therapy only, or no systemic adjuvant treatment. One of themajor benefits of the ROR predictor is the identification of LumApatients that are at a very low risk of relapse, and for whom thebenefit from adjuvant chemotherapy is unlikely. In this context the RORpredictor based on subtypes provides similar information as theOncotypeDx Recurrence Score for ER-positive, node negative patients (4,5). However the PAM50 based assay provides a risk of relapse score forall patients, including those with ER-negative disease.

In summary, this subtype predictor and ROR classifier effectivelyidentifies molecular features in breast tumors that are important forprognosis and treatment. The qRT-PCR assay can be performed usingarchived breast tissues, which will be useful for retrospective studiesand prospective clinical trials.

TABLE 2 Clinical and Subtype Data for Prototype Samples fromMicroarray/qRT-PCR Training Sets Pa- Subtype Overall tient GEOAssignment Dx Ethni- Sur- Vital ER PR HER2 ID accession (SigClust)qPCR_name Age city pT* pN{circumflex over ( )} M Grade^(%) vival Status(IHC)** (IHC)** (status){circumflex over ( )}{circumflex over ( )} 1GSM275694 Basal-like BR000161BPE_UU NA NA NA NA NA NA NA NA NA NA NA 3GSM140985 Basal-like BR000572BPE_UU 45 AA 3 0 0 3 NA NA 0 0 NA 7GSM140999 Basal-like BR010135BPE_UU 36 AA 1 0 0 3 49 0 0 0 0 10GSM275782 Basal-like BR010532BPE_UU 41 AA 3 1 0 3 14 1 0 0 0 11 GSM80221Basal-like BR020018BPE_UU 55 C 2 0 0 3 31 0 0 0 0 12 GSM141096 HER2-BR020155BPE_UU 38 NA 3 1 0 3 42 0 0 0 1 enriched 13 GSM141099 HER2-BR020306BPE_UU 42 C 4 2 1 3 22 1 1 1 1 enriched 14 GSM141102 LumBBR020439BPE_UU 53 C 4 1 1 3 16 1 1 0 0 15 GSM275783 LumA BR020464BPE_UU44 C 2 0 0 1 2 0 1 1 0 16 GSM141105 Basal-like BR020578BPE_UU 76 AA 4 00 3 1 0 0 1 18 GSM141110 Basal-like BR030459BPE_UU 30 C 3 0 0 3 37 0 0 00 19 GSM275771 LumA BR030584BPE_UU 54 C 1 1 NA NA NA 0 1 1 0 21GSM141114 LumB BR040114BPE_UU 56 C 2 0 0 2 16 0 1 1 0 22 GSM141117 LumBBR040182BPE_UU 88 C 2 1 0 3 16 0 1 1 1 23 GSM141121 HER2- BR040269BPE_UU46 C 2 0 0 3 17 0 0 0 1 enriched 28 GSM34523 Basal-like PB00205PE_UU 39C NA NA 1 3 5 1 0 0 0 29 GSM52895 LumA PB00284PE_UU 34 C 1 0 0 1 54 0 1I 0 30 GSM34565 Basal-like PB00297PE_UU 55 AA 2 0 0 3 55 0 0 0 0 31GSM34481 HER2- PB00311PE_UU 47 C 2 1 0 3 50 0 1 1 0 enriched 32 GSM34497HER2- PB00314PE_UU 50 C 3 1 0 3 52 0 0 0 1 enriched 33 GSM34527Basal-like PB00334PE_UU 50 AA 1 0 0 3 54 0 0 0 0 35 GSM34544 HER2-PB00376PE_UU 50 AA 2 0 0 3 49 0 0 0 0 enriched 37 GSM34549 LumAPB00441PE_UU 83 C 1 0 0 2 14 0 1 1 0 38 GSM34528 HER2- PB00455PE_UU 52AA 3 1 0 2 46 0 0 0 1 enriched 39 GSM52884 LumA PB00479PE_UU 50 NA 2 0 0NA NA 0 1 1 0 41 GSM50157 Basal-like UB00028PE_UU 46 C 1 0 0 3 59 0 0 00 42 GSM34437 Basal-like UB00029PE_UU 59 C 2 0 0 3 59 0 0 0 0 43GSM34431 HER2- UB00037PE_UU 42 C 1 1 0 3 58 0 0 1 0 enriched 44 GSM34548LumA UB00038PE_UU 50 C 1 0 0 2 57 0 1 1 0 47 GSM34428 LumA UB00044PE_UU49 C 2 1 0 2 59 0 1 1 0 50 GSM34557 LumA UB00056PE_UU 63 C 1 1 0 2 56 01 1 0 53 GSM34532 HER2- UB00060PE_UU 72 C 3 3 0 3 49 0 0 0 1 enriched 56GSM34450 Basal-like UB00067PE_UU 80 C 1 1 0 3 38 1 0 0 0 57 GSM34451LumA UB00069PE_UU 40 C 1 0 0 2 7 0 NA NA 0 58 GSM34452 Basal-likeUB00071PE_UU 60 C 1 0 0 3 50 0 0 0 0 60 GSM141079 LumA UB00081LPE_UU 65C NA 1 0 2 44 0 1 1 0 61 GSM141081 LumA UB00082PE_UU 43 C 1 1 0 1 40 0 11 0 63 GSM141084 LumB UB00088PE_UU 69 C 2 2 0 2 38 1 1 1 1 64 GSM141085LumA UB00091PE_UU 77 C 1 0 0 2 36 0 1 1 0 65 GSM141088 LumA UB00099PE_UU50 C 3 1 0 2 35 0 1 1 0 66 GSM141070 Basal-like UB00100PE_UU 49 C 1 0 03 34 0 NA NA 0 67 GSM141071 Basal-like UB00110PE_UU 76 C 2 0 0 3 31 0 00 0 68 GSM141072 Basal-like UB00116PE_UU 67 C 2 0 NA 3 34 0 0 0 1 69GSM141073 HER2- UB00117PE_UU 72 other NA 0 0 3 31 0 0 0 1 enriched 72GSM275802 Basal-like WU00328- 59 C 12 0 0 3 82 0 0 0 0 16563PE_UU 73GSM275803 HER2- WU00431- 73 C 4 1 0 3 44 1 0 0 1 enriched 16439PE_UU 74GSM275800 HER2- WU00441- 49 C 3 1 0 2 27 1 1 1 1 enriched 19793PE_UU 75GSM275804 LumA WU00509- 57 C 2 1 0 3 51 0 0 0 0 19794PE_UU 76 GSM275805HER2- WU00531- 75 C 2 0 0 1 81 0 1 0 0 enriched 19795PE_UU 78 GSM275807LumB WU00556- 46 C 2 1 0 2 88 0 1 1 0 21032PE_UU 82 GSM275810 Basal-likeWH00899- 47 AA 2 0 0 3 83 0 1 1 0 18760PE_UU 86 GSM275813 Basal-likeWU01407- 39 AA 2 0 0 3 80 0 0 0 0 16456PE_UU 88 GSM275815 HER2- WU01500-88 C 1 0 0 3 70 0 1 0 0 enriched 18755PE_UU 89 GSM275816 HER2- WU01502-74 C 2 2 0 3 88 0 1 0 0 enriched 16455PE_UU 90 GSM275817 HER2- WU01511-50 AA 1 1 0 3 82 0 0 0 1 enriched 19773PE_UU 91 GSM275818 LumA WU01520-58 C 2 1 0 2 77 NA 1 1 NA 21957PE_UU 92 GSM275819 HER2- WU01540- 46 C 31 0 3 20 1 NA NA 0 enriched 14690PE_UU 93 GSM275799 LumB WU01576- 64 O 21 0 3 56 0 1 1 0 19797PE_UU 95 GSM275821 LumB WU01587- 72 C 1 0 1 2 82 01 0 0 16348PE_UU 96 GSM275822 LumB WU01613- 78 C 2 2 0 3 17 0 1 1 016349PE_UU 97 GSM275823 Basal-like WU01680- 47 C 2 0 0 3 77 0 0 0 116347PE_UU 99 GSM275825 Basal-like WU01790- 32 AA 3 1 0 3 15 0 0 0 116344PE_UU 101 GSM275792 Basal-like WU01887- 73 AA 2 0 0 3 78 0 0 1 016342PE_UU 104 GSM275829 Basal-like WU02104- 57 C 2 1 0 3 51 0 0 0 016341PE_UU 105 GSM275830 Basal-like WU02132- 57 C 3 1 0 3 76 0 0 0 018761PE_UU 107 GSM275832 HER2- WU02338- 42 C 2 1 0 3 59 1 1 1 1 enriched21961PE_UU 108 GSM275833 Basal-like WU02390- 46 C 2 0 1 3 9 1 0 0 016330PE_UU 109 GSM275834 Basal-like WU02455- 44 C 3 0 0 3 15 1 0 0 014693P E_UU 110 GSM275797 HER2- WU02468- 63 AA 1 2 0 3 72 0 NA NA 1enriched 21279PE_UU 113 GSM275795 HER2- WU02769- 73 C 1 0 0 2 69 0 1 0 1enriched 16337PE_UU 114 GSM275837 Basal-like WU02771- 43 C 3 0 0 3 16 10 0 1 14694PE_UU 116 GSM275839 Basal-like WU02843- 46 C 1 0 0 3 75 0 0 01 19762PE_UU 118 GSM275841 Basal-like WU02948- 44 C 1 0 0 3 62 0 0 0 016566PE_UU 120 GSM275842 HER2- WU03064- 74 AA 2 0 0 3 70 0 1 1 1enriched 16462PE_UU 121 GSM275843 Basal-like WU03292- 50 AA 3 0 0 3 79 00 0 0 16446PE_UU 123 GSM275791 HER2- WU03456- 52 AA 1 0 0 3 67 0 1 1 0enriched 16361PE_UU 125 GSM275846 LumB WU03535- 82 C 2 0 0 3 60 0 1 1 016451PE_UU 126 GSM275796 HER2- WU03653- 49 C 1 2 0 3 102 0 0 0 1enriched 16448PE_UU 127 GSM275847 Basal-like WU03661- 53 AA 4 1 0 3 3 11 1 0 16447PE_UU 128 GSM275788 LumA WU03662- 75 AA 2 0 0 3 45 0 0 0 016452PE_UU 129 GSM275848 Basal-like WU03685- 42 AA 2 0 0 3 65 0 0 0 016502PE_UU 131 GSM275850 Basal-like WU03714- 66 AA 1 0 0 3 72 0 1 1 021262PE_UU 132 GSM275793 HER2- WU03721- 29 C 2 1 0 3 13 0 1 0 1 enriched16570PE_UU 134 GSM275852 Basal-like WU03791- 61 C 1 1 0 3 62 0 0 0 016497PE_UU 135 GSM275853 Basal-like WU03831- 51 AA 2 1 0 3 68 0 0 0 121959PE_UU 139 GSM275857 Basal-like WU03885- 52 AA 2 1 0 3 26 1 1 1 016469PE_UU 140 GSM275858 HER2- WU03946- 72 C 2 1 0 2 15 NA 0 1 1enriched 14842PE_UU 141 GSM275789 Basal-like WU04000- 49 AA 1 2 0 3 24 1NA NA 0 16466PE_UU 144 GSM275861 HER2- WU04038- 51 AA 2 1 0 2 62 0 1 1 1enriched 16465PE_UU 146 GSM275863 Basal-like WU04327- 73 C 1 0 0 2 69 01 0 1 19803PE_UU 147 GSM275864 LumB WU04532- 75 AA 2 1 0 3 3 0 1 0 116463PE_UU 148 GSM275865 Basal-like WU04834- 42 AA 2 0 0 3 65 0 0 0 016461PE_UU 149 GSM275866 Basal-like WU04952- 64 AA 2 1 0 3 62 0 0 0 019753PE_UU 152 GSM275872 LumA WU05094- 29 C 1 1 0 1 60 0 1 1 016580PE_UU 153 GSM275873 HER2- WU05118- 54 C 1 0 0 3 64 0 1 0 1 enriched19759PE_UU 155 GSM275875 HER2- WU05162- 43 C 2 0 0 3 95 1 NA NA NAenriched 21960PE_UU 156 GSM275876 Basal-like WU0519l- 51 AA 4 1 1 2 46 1NA NA 1 14791PE_UU 157 GSM275877 HER2- WU05196- 59 C 1 0 0 2 56 0 0 0 0enriched 16573PE_UU 158 GSM275878 HER2- WU05207- 72 AA 4 1 0 3 59 0 1 01 enriched 16473PE_UU 159 GSM275879 Basal-like WU05215- 79 AA 2 0 0 3 480 0 0 0 16503PE_UU 160 GSM275880 HER2- WU05337- 51 C 2 1 0 3 14 1 0 0 0enriched 14835PE_UU 161 GSM275881 HER2- WU05415- 52 C 2 0 0 3 48 0 1 1 1enriched 16357PE_UU 163 GSM275883 Basal-like WU05478- 41 C 2 0 0 3 54 00 0 0 16356PE_UU 165 GSM275885 Basal-like WU05641- 40 AA 2 0 0 3 54 0 00 0 16354PE_UU 167 GSM275887 Basal-like WU05991- 70 AA 4 1 0 3 19 1 0 00 14687PE_UU 168 GSM275888 Basal-like WU06036- 42 AA 2 0 0 3 50 0 0 0 NA16352PE_UU 169 GSM275889 Basal-like WU06397- 64 C 2 0 0 3 54 0 0 0 019805PE_UU 170 GSM275890 HER2- WU06398- 34 C 1 1 0 3 40 0 1 1 1 enriched19767PE_UU 171 GSM275891 HER2- WU06416- 74 C 1 0 0 3 51 0 0 0 0 enriched19781PE_UU 172 GSM275892 LumA WU06545- 69 C 3 0 0 3 49 0 1 1 018758PE_UU 173 GSM275893 HER2- WU06559- 42 AA 4 2 0 3 35 1 0 0 0enriched 14689PE_UU 174 GSM275894 Basal-like WU06580- 44 AA 2 0 0 3 48 00 0 0 16575PE_UU 175 GSM275895 LumB WU06611- 46 C 2 0 0 3 51 0 1 1 116475PE_UU 177 GSM275897 Basal-like WU06857- 40 AA 2 1 0 3 11 1 0 0 015260PE_UU 178 GSM275898 LumA WU07407- 66 C 2 1 0 3 5 0 1 1 0 19770PE_UU180 GSM275900 LumA WU07509- 57 AA 1 1 0 2 22 0 1 1 0 19782PE_UU 182GSM275902 LumA WU07512- 83 C 1 NA 0 2 21 0 1 1 0 14793PE_UU 184GSM275904 Basal-like WU07558- 41 AA 1 0 0 3 29 0 0 0 0 16584PE_UU 185GSM275905 Basal-like WU07589- 70 AA 2 1 0 3 19 0 0 0 0 16582PE_UU 187GSM275907 LumA WU07791- 78 C 1 0 0 2 53 0 1 1 1 19784PE_UU 188 GSM275908LumA WU07805- 41 C 2 1 0 3 58 0 1 1 0 19777PE_UU 189 GSM275909Basal-like WU08626- 34 AA 4 1 0 3 37 0 0 0 0 16506PE_UU 190 GSM34464Basal-like NA 61 NA 2 1 0 3 13 1 0 NA NA 191 GSM140992 Basal-like NA 29NA 2 2 0 3 NA 1 0 NA NA 192 GSM50148 Basal-like NA 51 C 4 2 0 NA 74 1 0NA NA 193 GSM34562 Basal-like NA 50 C 1 0 0 3 29 0 1 0 1 194 GSM52896LumB NA 79 NA 2 2 0 3 15 0 1 NA 1 195 GSM141067 Normal-like NA NA NA NANA NA NA NA NA NA NA NA 196 GSM80240 Normal-like NA NA NA NA NA NA NA NANA NA NA NA 197 GSM34547 Normal-like NA NA NA NA NA NA NA NA NA NA NA NA198 GSM34483 Normal like NA NA NA NA NA NA NA NA NA NA NA NA 199GSM34482 Normal-like NA NA NA NA NA NA NA NA NA NA NA NA 200 GSM140990Normal-like NA NA NA NA NA NA NA NA NA NA NA NA 201 GSM140991Normal-like NA NA NA NA NA NA NA NA NA NA NA NA 202 GSM275777Normal-like NA NA NA NA NA NA NA NA NA NA NA NA 203 GSM275778 Normallike NA NA NA NA NA NA NA NA NA NA NA NA 204 GSM275781 Normal-like NA NANA NA NA NA NA NA NA NA NA NA 205 GSM275780 Normal-like NA NA NA NA NANA NA NA NA NA NA NA 206 GSM275779 Normal-like NA NA NA NA NA NA NA NANA NA NA NA *pathologic tumor stage: T1 ≤ 2 cm, T2 > 2 cm-5 cm, T3 > 5cm, NA = not assessed {circumflex over ( )}pathologic node stage: N0 =no positive nodes, N1 = positive axillary nodes, NA = not assessed^(%)histological grade: 0 = grades 1&2, 1 = grade 3**immunohistochemistry: 0 = no to moderate staining, 1 = strong stainingin majority of cancer cells {circumflex over ( )}{circumflex over( )}immunonohistochemistry and fluorescence in-situ hybridization: 0 =negative by IHC (0, 1) or 2+ by IHC and negative by FISH, 1 = 3+ by IHCor 2+ by IHC and positive by FISH

TABLE 3 Clinical and Subtype Data for qRT-PCR Test Set Relapse SubtypeOverall Free Any ER PR Her2 Patient ID Prediction pT* pN{circumflex over( )} Grade^(%) Survival Survival Relapse*** DSS** (IHC){circumflex over( )}{circumflex over ( )} (IHC){circumflex over ( )}{circumflex over( )} (IHC){circumflex over ( )}{circumflex over ( )} 1001 LumB 2 1 12.3232877 0.690411 1 1 1 1 0 1002 Her2-enriched 2 NA 1 19.5123292.9753425 1 1 0 0 0 1003 Her2-enriched 1 1 1 7.0410959 5.4 1 1 0 NA 11004 LumB 2 0 0 NA NA NA 1 1 1 0 1005 Her2-enriched 1 0 0 0.9397260.3671233 1 1 0 NA NA 1006 LumB 2 1 0 0.7178082 0.5178082 1 2 1 1 0 1007LumB NA NA 0 3.3287671 1.7616438 1 1 1 1 0 1008 Her2-enriched 2 NA 00.8849315 0.7068493 1 1 1 0 0 1009 Basal-like 3 1 0 5.9917808 0.82465751 1 0 0 0 1010 LumA 2 1 NA 12.273973 9.0712329 1 1 1 1 NA 1011Her2-enriched 2 1 1 3.2027397 1.2493151 1 1 0 0 1 1012 Normal-like 1 1 025.435616 22.709589 1 1 1 1 0 1013 LumB 2 1 0 NA NA NA 1 1 0 0 1014 LumB2 0 0 16.654795 16.654795 0 2 1 1 0 1015 LumA 2 0 0 4.5150685 3.88219181 1 1 0 0 1016 Basal-like 2 0 1 2.0383562 1.6712329 1 1 0 0 0 1017 LumA2 1 0 10.331507 5.9315068 1 1 1 0 0 1018 LumB 2 0 0 22.230137 21.89589 11 1 1 0 1019 LumB 2 1 0 3.0931507 1.4520548 1 1 1 1 0 1020 LumA 2 1 04.8630137 4.8630137 0 2 1 1 0 1021 LumA 1 0 0 6.7972603 4.9671233 1 2 11 0 1022 LumB 2 1 0 3.9150685 1.4164384 1 1 1 0 0 1023 LumA 2 1 025.945205 25.945205 0 3 1 1 0 1024 Basal-like 3 NA 1 2.4438356 1.77534251 1 0 0 0 1025 LumA 1 1 0 2.8767123 0.0027397 1 1 1 0 1 1026 LumA 1 NA 08.0821918 8.0821918 0 2 1 1 0 1027 LumB 1 1 0 25.778082 25.778082 0 3 11 0 1028 LumA 2 1 0 9.2520548 8.9753425 1 1 1 1 0 1029 Basal-like 2 0 13.4410959 1.9726027 1 1 0 0 0 1030 Her2-enriched 2 1 0 2.92328771.709589 1 1 1 1 1 1031 LumA 1 1 0 2.9616438 2.8958904 1 1 1 0 0 1032LumA 2 0 0 4.509589 0.8465753 1 1 1 1 0 1033 LumB 1 NA 1 10.3123299.9780822 1 1 NA NA NA 1034 LumB 1 0 1 15.19726 15.19726 0 2 1 1 0 1035Basal-like 1 1 1 25.339726 25.339726 0 3 1 1 0 1036 LumA 2 1 0 3.44657531.460274 1 1 1 NA 0 1037 Basal-like 1 0 0 11.958904 11.958904 0 2 0 0 01038 Basal-like 2 1 1 2.4849315 2.2082192 1 1 0 NA 0 1039 LumA 2 NA 08.539726 6.8136986 1 1 1 1 0 1040 Basal-like 2 0 0 25.090411 25.090411 03 0 0 0 1041 LumA 2 1 0 3.7369863 1.7643836 1 1 NA NA 0 1042 Basal-like1 NA 0 2.1780822 0.9561644 1 1 1 1 1 1043 LumB 2 1 0 2.5452055 0.52328771 1 1 1 0 1044 LumA 1 1 0 2.630137 0.7315068 1 1 1 1 0 1045 Basal-like 21 1 1.4109589 1.060274 1 1 0 0 0 1046 Basal-like 2 1 1 24.83561624.835616 0 3 0 0 0 1047 Basal-like 2 0 1 14.873973 14.536986 1 1 0 0 01048 Her2-enriched 1 1 1 2.3917808 1.5506849 1 1 1 1 0 1049 LumA 2 1 019.339726 19.339726 0 2 1 0 0 1050 LumB 2 1 0 13.605479 13.605479 0 2 10 NA 1051 LumB 2 1 0 2.4191781 1.4520548 1 1 1 1 0 1052 Basal-like 2 1 012.073973 11.739726 1 1 NA NA 0 1053 Basal-like 2 1 0 0.35068490.0027397 1 1 0 0 0 1054 Basal-like 1 0 1 24.449315 24.449315 0 3 0 0 01055 LumB 2 0 0 6.1589041 4.1643836 1 1 1 0 0 1056 LumB 2 NA 0 1.25753420.0109589 1 1 1 1 0 1057 LumA 2 0 0 7.7753425 5.6630137 1 1 1 1 0 1058Basal-like 1 1 1 24.323288 24.323288 0 3 0 NA NA 1059 LumB 2 1 05.9863014 5.0876712 1 1 1 1 0 1060 LumB 1 1 1 24.115068 24.115068 0 3 11 0 1061 Basal-like 2 1 1 4.7972603 4.7972603 0 2 0 0 0 1062 Basal-like3 1 1 24.084932 24.084932 0 3 0 0 0 1063 Basal-like 2 0 0 24.01917824.019178 0 3 1 0 0 1064 Basal-like 2 0 1 22.791781 22.791781 0 3 0 NANA 1065 Her2-enriched 2 0 0 9.5150685 9.5150685 0 NA 0 0 0 1066Basal-like 1 0 1 20.945205 20.945205 0 3 0 0 0 1067 Normal-like 2 0 010.917808 10.917808 0 2 NA NA 1 1068 Her2-enriched 2 0 0 6.70136992.9726027 1 1 0 NA 0 1069 LumB 3 0 0 11.912329 11.912329 0 2 1 1 0 1070LumB 2 0 0 20.731507 17.008219 1 3 1 1 0 1071 LumB 1 NA 0 4.01917811.2493151 1 2 1 1 0 1072 LumA 2 1 0 12.441096 12.441096 0 2 1 1 0 1073Her2-enriched 3 0 0 20.660274 20.660274 0 3 NA NA 1 1074 LumA 1 NA 04.7835616 4.4465753 1 1 1 0 0 1075 LumA 2 0 0 2.7534247 2.4246575 1 1 11 0 1076 LumA 2 1 0 20.539726 20.539726 0 3 1 NA 0 1077 LumA 1 1 020.408219 12.328767 1 3 1 1 0 1078 Normal-like 2 NA 0 2.66301371.2493151 1 1 1 NA 0 1079 Her2-enriched 1 1 0 NA NA NA 1 0 0 1 1080Basal-like 2 1 0 NA NA NA 3 1 1 0 1081 Normal-like 2 1 0 NA NA NA 1 1 10 1082 Her2-enriched 2 0 1 NA NA NA 1 1 1 1 1083 LumB 2 0 0 NA NA NA 2 11 NA 1084 LumA 1 0 0 NA NA NA 3 NA NA 0 1085 Basal-like 3 0 0 NA NA NA 11 0 0 1086 Basal-like 1 0 1 19.969863 19.969863 0 3 0 0 0 1087Basal-like 1 0 0 19.657534 19.657534 0 3 0 0 0 1088 LumA 1 0 0 16.2383563.9616438 1 1 1 1 0 1089 Her2-enriched 2 0 1 19.506849 19.506849 0 3 1 11 1090 LumA 1 0 0 8.5945205 6.9041096 1 1 0 0 0 1091 LumA 1 0 019.432877 2.6082192 1 3 1 1 0 1092 Basal-like 1 0 NA 19.405479 19.4054790 3 0 NA NA 1093 LumB 2 0 0 19.408219 16.756164 1 3 1 NA 0 1094 LumA 1NA 0 19.358904 19.358904 0 3 1 1 0 1095 LumB 2 0 0 19.353425 19.353425 03 1 0 0 1096 LumA 2 0 0 13.345205 13.345205 0 2 1 1 0 1097 Her2-enriched2 0 0 8.3890411 5.290411 1 2 1 0 0 1098 Her2-enriched 1 0 0 6.58630143.8767123 1 1 0 NA 1 1099 LumB 2 0 1 3.7780822 3.4438356 1 1 1 0 1 1100Her2-enriched 1 0 0 19.20274 19.20274 0 3 1 0 1 1101 Basal-like 2 0 119.186301 19.186301 0 3 0 0 1 1102 Basal-like 1 0 0 5.8410959 4.75890411 1 0 NA NA 1103 LumB 1 0 0 17.734247 3.6876712 1 2 1 NA NA 1104 LumA 20 1 9.7917808 9.7917808 0 2 1 NA 0 1105 Basal-like 1 0 1 19.10684919.106849 0 3 0 NA 0 1106 Basal-like 2 0 1 19.09863 19.09863 0 3 0 NA NA1107 LumA 1 0 0 19.871233 19.871233 0 3 1 1 0 1108 Basal-like 1 0 119.808219 19.808219 0 3 1 0 NA 1109 LumB 2 0 0 19.791781 1.6794521 1 3 11 0 1110 Her2-enriched 2 0 0 16.778082 16.778082 0 2 1 0 0 1111 LumA 1 00 19.789041 19.789041 0 3 1 1 0 1112 LumB 2 0 0 3.5068493 3.3643836 1 11 1 0 1113 LumB 2 0 1 2.3150685 0.9369863 1 2 1 0 0 1114 Basal-like 2 01 3.3232877 3.3232877 1 1 0 NA 0 1115 Her2-enriched 2 0 1 19.98630119.986301 0 3 1 1 0 1116 Basal-like 2 0 1 NA NA NA 2 0 0 0 1117Her2-enriched 1 0 1 19.758904 19.758904 0 3 1 NA 1 1118 LumB 1 0 119.717808 19.717808 0 3 1 1 0 1119 LumB 1 0 0 4.4054795 2.460274 1 1 1NA 0 1120 LumB 2 0 1 5.3342466 4.8794521 1 1 1 1 0 1121 LumA 1 0 011.531507 11.531507 0 2 1 1 0 1122 Normal-like 1 0 0 15.424658 14.8246581 2 NA NA NA 1123 LumB 2 0 0 3.8876712 3.8794521 1 1 1 1 NA 1124Basal-like 1 0 0 19.252055 19.252055 0 3 0 0 0 1125 Normal-like 1 0 019.205479 19.205479 0 3 1 1 0 1126 LumB 1 0 0 20.005479 20.005479 0 3 11 0 1127 LumA 1 0 0 19.950685 19.950685 0 3 1 NA 0 1128 Normal-like 2 10 19.931507 2.2657534 1 3 1 NA 0 1129 LumA 1 0 0 19.849315 4.509589 1 31 1 0 1130 Basal-like NA 0 1 8.0630137 8.0630137 0 NA 0 NA NA 1131Basal-like 2 1 1 1.030137 0.0520548 1 1 0 0 1 1132 Normal-like 1 0 019.608219 19.608219 0 3 1 1 0 1133 Her2-enriched 1 1 1 19.55068519.550685 0 3 1 1 0 1134 Basal-like 2 1 0 2.0712329 1.230137 1 1 NA NANA 1135 LumA 1 1 0 19.449315 19.449315 0 3 1 1 0 1136 LumB 1 1 05.0520548 4.4684932 1 1 1 1 0 1137 LumA 2 1 0 19.331507 17.657534 1 3 11 0 1138 LumA 1 1 0 19.331507 19.331507 0 3 1 1 0 1139 Basal-like 1 0 119.046575 19.046575 0 3 NA 0 0 1140 Her2-enriched 1 0 0 18.91780818.917808 0 3 1 0 0 1141 LumB 3 NA 0 5.5205479 1.8410959 1 1 1 1 0 1142LumA 1 0 0 18.649315 18.649315 0 NA 1 NA 0 1143 LumA 1 0 0 4.88767124.8876712 0 1 1 1 0 1144 Basal-like NA 1 0 2.9972603 2.9123288 1 1 NA NA0 1145 LumA 1 0 0 18.753425 18.753425 0 3 1 1 NA 1146 LumA 3 NA 03.1917808 0.0027397 1 1 1 1 0 1147 Basal-like 1 0 1 10.79726 10.79726 02 1 0 0 1148 LumB 1 1 0 13.542466 4.6027397 1 2 1 1 0 1149 LumB 2 1 018.715068 18.715068 0 3 1 1 0 1150 Her2-enriched 2 1 1 2.60273972.0931507 1 1 0 NA 1 1151 LumB NA NA 1 18.641096 18.641096 0 3 1 1 01152 LumA 1 0 0 18.621918 18.621918 0 3 1 1 0 1153 LumA NA NA 0 1.4602741.460274 0 NA 1 NA 0 1154 LumA 2 0 0 7.3479452 7.3479452 0 2 1 1 0 1155LumA 1 1 0 7.4246575 6.939726 1 2 1 1 0 1156 Normal-like 2 0 0 18.45205518.452055 0 3 0 0 0 1157 Her2-enriched 2 1 1 4.6246575 3.7671233 1 1 NA0 NA 1158 LumA 1 1 0 7.3890411 8.060274 1 1 1 NA 0 1159 Her2-enriched 3NA 1 18.986301 0.9863014 1 3 1 NA 1 1160 Her2-enriched 2 0 1 17.96986317.969863 0 3 1 0 NA 1161 LumB 2 1 0 4.1452055 4.1452055 0 2 1 0 0 1162LumA 1 1 0 17.909589 17.909589 0 3 1 1 0 1163 LumA 2 1 0 9.39726039.3972603 0 2 1 1 0 1164 LumA 2 0 0 7.8109589 7.8109589 0 2 1 1 0 1165Her2-enriched 2 1 0 NA NA NA 1 0 0 NA 1166 Basal-like 1 0 1 17.37808217.378082 0 3 0 0 0 1167 Her2-enriched NA NA 0 17.071233 17.071233 0 3 10 0 1168 LumA 1 0 NA 17.161644 17.161644 0 3 1 1 0 1169 Basal-like 3 NA1 10.742466 6.5315068 1 1 0 NA 0 1170 Basal-like 1 NA 1 NA NA NA 1 0 0 01171 Her2-enriched 1 1 1 4.8438356 2.7506849 1 2 1 1 1 1172 LumB 1 NA 07.3972603 7.3972603 0 2 1 1 NA 1173 LumB 1 0 1 16.934247 3.4219178 1 3 11 0 1174 LumA 1 0 0 16.90137 16.90137 0 3 1 1 0 1175 LumB 2 1 016.882192 16.882192 0 3 1 1 0 1176 Her2-enriched 2 NA 1 9.12328779.1232877 0 2 1 1 1 1177 Basal-like 1 1 1 2.0986301 0.6547945 1 1 0 0 01178 Her2-enriched 2 0 1 2.1534247 1.7506849 1 1 0 0 0 1179 Basal-like 1NA 1 0.0493151 0.0027397 1 1 0 0 0 1180 LumB 1 0 0 8.0328767 4.7917808 11 1 0 0 1181 LumA 1 0 0 7.8383562 7.8383562 0 2 1 NA 0 1182Her2-enriched 3 1 0 16.706849 16.706849 0 3 0 0 0 1183 Basal-like 2 1 03.3835616 1.0547945 1 1 1 1 NA 1184 Basal-like 2 0 1 16.547945 16.5479450 3 0 0 1 1185 Her2-enriched 2 0 0 16.520548 16.520548 0 3 1 1 1 1186Normal-like 2 1 0 1.7506849 1.7506849 0 2 1 NA 0 1187 Her2-enriched 3 NA0 1.7150685 0.0082192 1 1 NA 0 1 1188 Basal-like 2 1 1 16.4794521.8219178 1 3 0 0 0 1189 Normal-like 2 0 0 11.153425 7.0520548 1 1 NA NA0 1190 LumA 2 0 0 16.287671 16.287671 0 3 1 1 0 1191 LumB 2 0 016.345205 16.345205 0 3 1 1 0 1192 LumB 1 0 0 16.227397 16.227397 0 3 1NA NA 1193 LumA NA NA 0 6.2821918 6.2821918 0 2 1 1 0 1194 LumA 3 NA 04.2767123 1.2849315 1 1 1 1 0 1195 LumA 2 0 0 9.7479452 9.7479452 0 2 11 NA 1196 LumB 1 NA 0 7.0684932 7.0684932 0 2 1 0 0 1197 LumA 1 0 05.8027397 5.8027397 0 2 1 0 0 1198 LumA 2 1 0 1.0383562 1.0383562 0 2 1NA 0 1199 LumB 2 0 0 7.8520548 7.5178082 1 1 1 1 0 1200 LumA 2 0 015.863014 15.863014 0 3 1 1 0 1201 Normal-like 1 0 0 15.693151 15.6931510 3 1 0 0 1202 LumB 1 NA 1 0.030137 0.030137 0 2 1 0 0 1203Her2-enriched 2 1 0 10.046575 10.046575 0 2 0 0 0 1204 LumA 2 0 015.210959 15.210959 0 3 1 1 0 1205 LumA 1 NA 0 15.189041 10.890411 1 3 11 0 1206 LumA 2 0 0 15.169863 15.169863 0 3 1 NA NA 1207 LumA 1 0 NA2.2794521 0.9726027 1 1 1 NA 0 1208 Her2-enriched 1 0 1 8.07123296.0547945 1 1 0 0 1 1209 Basal-like 2 1 0 4.1287671 3.8630137 1 1 1 0 01210 LumB 1 0 0 15.035616 15.035616 0 3 1 1 0 1211 LumB 3 NA 0 1.97808220.9123288 1 1 1 1 0 1212 Basal-like 1 1 0 15.032877 15.032877 0 3 1 1 01213 Her2-enriched 2 1 0 15 15 0 3 0 0 0 1214 Basal-like 1 0 1 14.96712314.967123 0 3 0 NA 0 1215 Normal-like 1 1 0 12.073973 12.073973 0 2 1 0NA 1216 LumA 2 0 1 8.8 8.7835616 1 2 1 1 0 1217 LumA 1 1 0 14.8986314.375342 1 3 1 1 0 1218 LumA 2 1 0 7.6767123 6.4410959 1 1 1 NA 0 1219LumA 3 1 0 4.1890411 1.6493151 1 1 1 1 0 1220 Basal-like 3 1 0 2.51506850.4109589 1 1 0 0 0 1221 Normal-like 1 0 1 14.852055 14.852055 0 3 0 NANA 1222 LumA 1 0 0 14.772603 14.772603 0 3 NA NA 0 1223 Basal-like 3 1 11.5589041 1.3260274 1 2 0 0 0 1224 Normal-like 1 0 0 14.709589 14.7095890 3 NA NA 0 1225 Basal-like 2 1 0 14.69589 13.29589 1 3 1 1 0 1226 LumA3 NA 0 2.5232877 0.0410959 1 1 1 0 0 1227 LumA 1 0 0 14.578082 14.5780820 3 1 0 0 1228 LumB 2 0 0 14.572603 14.572603 0 3 1 1 0 1229Her2-enriched 2 0 1 14.613699 14.613699 0 3 0 0 0 1230 LumA 2 0 02.0109589 2.0109589 0 2 1 1 0 1231 LumA 1 0 0 14.542466 14.542466 0 3 10 0 1232 Her2-enriched 1 0 1 14.534247 14.534247 0 3 1 1 0 1233 LumB 1NA 0 4.9123288 4.0849315 1 1 1 1 0 1234 LumB NA 1 0 5.4876712 5.48767120 2 1 NA 0 1235 LumA 1 0 0 14.641096 14.641096 0 3 1 1 0 1236 LumA 2 0 014.520548 14.520548 0 3 1 1 0 1237 LumA 2 0 0 7.3780822 5.0109589 1 2 11 0 1238 Her2-enriched 3 NA 1 NA NA NA 1 1 0 0 1239 Her2-enriched 2 1 014.465753 14.465753 0 3 1 NA NA 1240 Basal-like 3 1 0 14.43835614.438356 0 3 1 NA NA 1241 LumA 1 0 0 14.421918 14.421918 0 3 1 1 0 1242LumA 2 0 0 14.419178 14.419178 0 3 1 NA NA 1243 LumB 1 0 0 14.40821914.408219 0 3 1 1 0 1244 LumB 1 1 0 10.013699 10.013699 0 2 1 1 0 1245LumA 2 0 0 14.383562 14.383562 0 3 1 1 0 1246 LumB 2 0 0 4.56438364.5643836 0 2 1 1 0 1247 LumB 1 0 0 14.312329 1.4739726 1 3 1 1 0 1248Normal-like 1 1 0 14.249315 14.249315 0 3 NA NA 0 1249 LumA 3 0 013.49863 13.49863 0 2 1 0 0 1250 Basal-like NA 0 1 14.235616 14.235616 03 0 NA 0 1251 LumB 1 0 0 14.945205 14.945205 0 3 1 0 0 1252 Normal-like1 0 0 1.6547945 1.2493151 1 1 NA NA 0 1253 LumA 2 1 0 4.21643844.2164384 0 2 1 0 0 1254 Normal-like 1 0 0 12.526027 12.526027 0 3 1 NA0 1255 LumB 2 1 0 12.463014 12.463014 0 3 1 1 0 1256 Basal-like 2 1 03.7205479 1.7452055 1 1 1 1 NA 1257 Normal-like 1 0 0 12.42739712.427397 0 3 1 NA 0 1258 LumA 2 0 0 12.372603 12.372603 0 3 1 NA 0 1259LumA 1 1 0 12.328767 12.328767 0 3 1 NA NA 1260 Basal-like 1 1 012.29589 2.5945205 1 3 1 1 0 1261 LumA 2 0 0 12.312329 12.312329 0 3 1NA 0 1262 Normal-like 1 0 0 12.180822 12.180822 0 3 1 NA 0 1263Basal-like 1 0 0 12.2 12.2 0 3 NA NA NA 1264 Her2-enriched 1 0 1 3.62.0684932 1 1 1 1 0 1265 LumA 2 1 0 12.2 12.2 0 3 1 1 0 1266 Normal-like1 1 NA 11.857534 11.857534 0 3 1 NA 0 1267 Her2-enriched 2 0 1 11.18630111.186301 0 3 0 0 0 1268 Normal-like 1 1 NA 11.073973 11.073973 0 3 1 NA0 1269 Normal-like 3 0 0 10.969863 10.969863 0 3 1 1 0 1270 LumA 2 NA 010.920548 10.920548 0 3 1 1 0 1271 LumA 2 1 0 10.79726 10.79726 0 3 1 00 1272 LumB 1 0 0 10.668493 10.668493 0 3 1 0 0 1273 LumA 2 1 010.167123 10.167123 0 3 1 1 NA 1274 LumA 1 0 0 9.6821918 9.6821918 0 3 1NA 0 1275 Normal-like 2 0 0 9.5917808 9.5917808 0 3 1 1 0 1276Normal-like 1 0 0 9.6082192 9.6082192 0 3 1 1 0 1277 Basal-like 2 0 19.5287671 9.5287671 0 3 0 NA 0 1278 Normal-like 1 0 0 6.58356166.2547945 1 1 1 1 NA 1279 Basal-like 2 1 0 9.3643836 9.3643836 0 3 NA NA0 *tumor size: T1 ≤2 cm, T2 >2 cm-5 cm, T3 >5 cm, NA = not assessed{circumflex over ( )}nodal status: 0 = node negative, 1 = node positive,NA = nodal status unknown ^(%)Nottingham histological grade: 0 = grades1&2, 1 = grade 3, NA = unknown ***any relapse free survival: 0 = norelapse, 1 = relapse **disease specific survival: 1 = death from breastCA, 2 = death from other than breast CA, 3 = alive, NA = unknown{circumflex over ( )}{circumflex over ( )}immunohistochemistry biomarker0 1 NA ER <1% positive nuclei ≥1% positive nuclei uninterpretable PR <1%positive nuclei ≥1% positive nuclei uninterpretable Her2-enrichednegative or weak expression strong expression uninterpretable

TABLE 4 Source Data for qRT-PCR and Microarray Datasets Number N-,Number of no adjuvant Endocrine GEO Accessions (or Use in Subtype Use inRisk systemic Therapy Author Samples Platform other availability)Classification Prediction therapy Only % ER+ Parker et al 189 qRT-PCR —— 0 0 54% Parker et al 279 qRT-PCR Test Test 0 0 62% Parker et al 544Agilent GSE10886 Common to qRT- 355 in Test 31 27 56% Custom, 1A, PCRfor Training 1Av2 (189); others in Test (355) Hess et al 133 Affymetrixbioinformatics mdanderson.org/ Test Test 0 0 62% U133A pubdata Ivshinaet al 289 Affymetrix GSE4922 Test Test 142 66 86% U133A Loi et al 414Affymetrix GSE6532 Test Test 137 277 89% U133A & U133 + 2 van de Vijver295 Agilent GSE2845 Test Untreated for 165 20 76% et al Training (165);others in Test(130) Wang et al 286 Affymetrix GSE2034 Test Test 286 073% U133A

TABLE 5 Multivariate and univariate analyses using 1451 samples from acombined microarray test set with clinical data Multivariate*Multivariate* Multivariate*†‡ Univariate (subtype) (clinical) (subtype +clinical) Variable Co-efficient p-value{circumflex over ( )} Coefficientp-value{circumflex over ( )} Coefficient p-value{circumflex over ( )}Coefficient p-value{circumflex over ( )} Basal-like 0.14 0.25 0.125.10E−01 — −0.11 5.50E−01 HER-enriched 0.62 1.00E−08 0.53 1.60E−03 —0.35 4.00E−02 LumA −0.94 1.00E−22 −0.67 6.20E−05 — −0.64 1.60E−04 LumB0.42 5.60E−06 0.3 5.50E−02 — 0.24 1.30E−01 ER Status −0.47 1.80E−06 — —−0.5 5.50E−07 −0.37 3.00E−03 Tumor Size 0.62 3.50E−12 — — 0.54 6.10E−090.47 5.30E−07 Node Status 0.37 2.80E−05 — — 0.24 1.10E−02 0.19 5.00E−02*Normal-like class used as reference state {circumflex over( )}Significant variables are in italics †p = 4e−10 (by the likelihoodratio test) for comparison with the Subtype model ‡= 2e−13 (by thelikelihood ratio test) for comparison with the Clinical model

TABLE 6 Distribution of Intrinsic Subtypes by ER-status % HER2- Test SetER-status # Samples % LumA % LumB enriched % Basal-like % Normal-likeUNC ER-positive 137 44% 35% 7% 4% 9% ER-negative 107 7% 5% 19% 51% 18%Hess et al ER-positive 82 44% 32% 10% 1% 13% ER-negative 51 2% 2% 41%51% 4% Ivshina et al ER-positive 211 42% 29% 11% 8% 9% ER-negative 34 9%15% 35% 38% 3% Loi et al ER-positive 349 39% 38% 8% 7% 8% ER-negative 4518% 9% 33% 27% 13% van de Vijver et al ER-positive 225 39% 31% 14% 4%12% ER-negative 70 1% 0% 31% 64% 3% Wang et al ER-positive 209 35% 33%11% 8% 13% ER-negative 77 5% 3% 29% 57% 6%

TABLE 7 T/FAC pathological complete response rates for PAM50 subtypesand triple-negative classification Classification RD pCR Basal-like 11(41%) 16 (59%) HER2-enriched 17 (59%) 12 (41%) LumA  36 (100%) 0 (0%)LumB 22 (82%)  5 (18%) Normal-like 13 (93%) 1 (7%) Triple Negative 13(50%) 13 (50%) Any positive 82 (80%) 20 (20%) Triple Negative/Basal  6(35%) 11 (65%) Triple Negative/Non-Basal  7 (78%)  2 (22%) Non-TripleNegative/Basal  4 (50%)  4 (50%) Non-Triple Negative/Non- 78 (83%) 16(17%) Basal *Percentages are calculated by the total per classification

REFERENCES

-   1. Sorlie T, Perou C M, Tibshirani R, et al: Gene expression    patterns of breast carcinomas distinguish tumor subclasses with    clinical implications. Proc Natl Acad Sci USA 98:10869-74, 2001-   2. van't Veer U, Dai H, van de Vijver M J, et al: Gene expression    profiling predicts clinical outcome of breast cancer. Nature    415:530-6, 2002-   3. van't Veer U, Paik S, Hayes D F: Gene expression profiling of    breast cancer: a new tumor marker. J Clin Oncol 23:1631-5, 2005-   4. Paik S, Shak S, Tang G, et al: A multigene assay to predict    recurrence of tamoxifen-treated, node-negative breast cancer. N Engl    J Med 351:2817-26, 2004-   5. Paik S, Tang G, Shak S, et al: Gene expression and benefit of    chemotherapy in women with node-negative, estrogen receptor-positive    breast cancer. J Clin Oncol 24:3726-34, 2006-   6. Hu Z, Fan C, Oh D S, et al: The molecular portraits of breast    tumors are conserved across microarray platforms. BMC Genomics 7:96,    2006-   7. Loi S, Haibe-Kains B, Desmedt C, et al: Definition of clinically    distinct molecular subtypes in estrogen receptor-positive breast    carcinomas through genomic grade. J Clin Oncol 25:1239-46, 2007-   8. Perou C M, Sorlie T, Eisen M B, et al: Molecular portraits of    human breast tumours. Nature 406:747-52, 2000-   9. Sorlie T, Tibshirani R, Parker J, et al: Repeated observation of    breast tumor subtypes in independent gene expression data sets. Proc    Natl Acad Sci USA 100:8418-23, 2003-   10. Fan C, Oh D S, Wessels L, et al: Concordance among    gene-expression based predictors for breast cancer. N Engl J Med    355:560-9, 2006-   11. Perreard L, Fan C, Quackenbush J F, et al: Classification and    risk stratification of invasive breast carcinomas using a real-time    quantitative RT-PCR assay. Breast Cancer Res 8:R23, 2006-   12. Eisen M B, Spellman P T, Brown P O, et al: Cluster analysis and    display of genome-wide expression patterns. Proc Natl Acad Sci USA    95:14863-8, 1998-   13. Yufeng L, Hayes D L, Nobel A, et al: Statistical significance of    clustering for high dimension low sample size data. Journal of the    American Statistical Association, in press-   14. Mullins M, Perreard L, Quackenbush J F, et al: Agreement in    breast cancer classification between microarray and quantitative    reverse transcription PCR from fresh-frozen and formalin-fixed,    paraffin-embedded tissues. Clin Chem 53:1273-9, 2007-   15. Storey J D, Tibshirani R: Statistical methods for identifying    differentially expressed genes in DNA microarrays. Methods Mol Biol    224:149-57, 2003-   16. Dudoit S, Fridlyand J: A prediction-based resampling method for    estimating the number of clusters in a dataset. Genome Biol    3:RESEARCH0036, 2000-   17. Tibshirani R, Hastie T, Narasimhan B, et al: Diagnosis of    multiple cancer types by shrunken centroids of gene expression. Proc    Natl Acad Sci USA 99:6567-72, 2002-   18. Dabney A R: Classification of microarrays to nearest centroids.    Bioinformatics 21:4148-54, 2005-   19. Hess K R, Anderson K, Symmans W F, et al: Pharmacogenomic    predictor of sensitivity to preoperative chemotherapy with    paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in    breast cancer. J Clin Oncol 24:4236-44, 2006-   20. van de Vijver M J, He Y D, van't Veer U, et al: A    gene-expression signature as a predictor of survival in breast    cancer. N Engl J Med 347:1999-2009, 2002-   21. Chaudhuri P, Marron J S: SiZer for Exploration of Structures in    Curves. Journal of the American Statistical Association 94:807-823,    1999-   22. Sheather S J, Jones M C: A Reliable Data-Based Bandwidth    Selection Method for Kernel Density Estimation. Journal of the Royal    Statistical Society 53:683-690, 1991 23. Neve R M, Chin K, Fridlyand    J, et al: A collection of breast cancer cell lines for the study of    functionally distinct cancer subtypes. Cancer Cell 10:515-27, 2006-   24. Rouzier R, Pusztai L, Delaloge S, et al: Nomograms to predict    pathologic complete response and metastasis-free survival after    preoperative chemotherapy for breast cancer. J Clin Oncol 23:8331-9,    2005-   25. Herschkowitz J I, Simin K, Weigman V J, et al: Identification of    conserved gene expression features between murine mammary carcinoma    models and human breast tumors. Genome Biol 8:R76, 2007-   26. Rakha E, Ellis I, Reis-Filho J: Are triple-negative and    basal-like breast cancer synonymous? Clin Cancer Res 14:618; author    reply 618-9, 2008-   27. Cheang M C, Voduc D, Bajdik C, et al: Basal-like breast cancer    defined by five biomarkers has superior prognostic value than    triple-negative phenotype. Clin Cancer Res 14:1368-76, 2008

Example 2 Introduction and Background Data

This technology also covers the use of the PAM50-based intrinsic subtypeclassifier as a predictive and prognostic signature in the neoadjuvantendocrine therapy setting. Postmenopausal patients with Stage 2 and 3 ERand/or PgR positive breast cancer can be treated with an endocrineagent, typically an aromatase inhibitor or tamoxifen, before surgery toimprove clinical outcomes, i.e., to promote the use of breast conservingsurgery or to improve operability in the setting of a tumor that hasinvaded into the tissues surrounding the breast. A predictive test toincrease the confidence that an individual patient will respond toneoadjuvant endocrine therapy is a significant advance.

Summary

The PAM50 based intrinsic subtype and proliferation-weighted risk score,when applied to samples from ER+breast cancers harvested afterinitiating treatment with an endocrine agent, can be used to predictresponse to neoadjuvant endocrine therapy and determine the prognosisfor patients with ER+breast cancer who will undergo long term therapywith an endocrine agent. A prognostic gene expression model trained ontumor samples taken before treatment (PAM50 proliferation weighted riskscore—described elsewhere herein) was applied to samples taken after theinitiation of neoadjuvant endocrine therapy. This approach is uniquebecause previous studies on the interaction of gene expression profilesand prognosis have only examined pretreatment samples and have neverapplied these models to post treatment samples. The prognostic andpredictive properties of the PAM50 intrinsic subtype and proliferationweighted prognostic model in baseline samples is compared to the samemodels applied to samples taken one month after initiating neoadjuvantendocrine therapy. Application of the PAM50 intrinsic subtype andproliferation-weighted risk of relapse model to the one month ontreatment samples accurately identifies aggressive tumors that fail torespond to neo adjuvant or adjuvant endocrine treatment. Patients withthese tumors should be immediately triaged to alternative neoadjuvanttreatments, such as chemotherapy, because these poor tumors behave asendocrine therapy refractory aggressive disease. A high degree ofcorrelation was established between the Ki67 proliferation marker andthe proliferation weighted PAM50 risk score supporting the claim thatthe PAM50 proliferation weighted risk score has prognostic properties.However these prognostic properties are markedly enhanced when theanalysis is applied to samples harvested from tumors that have beenexposed to an endocrine agent. In practice this can be easily achievedby prescribing an endocrine agent for a few weeks before definitivesurgery or by re-sampling a tumor early in the course of neoadjuvantendocrine treatment in order to identify unresponsive tumors.

Methodology:

The evidence to support these claims arises from a National CancerInstitute sponsored Phase 2 trial of neoadjuvant therapy with thearomatase inhibitor letrozole (NCI Grant No. RO1 CA095614). Eligibilityfor the trial required postmenopausal women with ER and/or PgR positiveStage 2 and 3 breast cancers. Patients received 4 months of therapy andthen they underwent surgery. Frozen tumor samples were obtained atbaseline, one month and at surgery. The samples were analyzed byfrozen-section and RNA was extracted using standard methodologies fromtumor rich specimens and subjected to gene expression analysis usingAgilent 1X44K arrays. The data was normalized to the data set used totrain the PAM50 classifier (methods described above) and two readoutswere produced: An intrinsic classification (LumA, LumB, HER2-enriched,Basal-like and Normal-like) and a proliferation weighted PAM50 riskscore. The aim of this study was to correlate the outcomes ofneoadjuvant endocrine therapy with the intrinsic classification and theproliferation weighted risk score derived from both the baseline sampleand the on treatment sample taken at one month.

Results:

The PAM50 intrinsic subtype and proliferation weighted risk score showedmarked changes at one month post therapy (Table 8). Most of thetransitions occurred in the LumB group with the majority shifting toLumA, but 16% remained in the LumB category despite treatment. Incontrast, most LumA tumors stayed LumA post therapy. These transitionswere due to the suppression of the proliferation cluster in the LumBgroup since the PAM50 proliferation weighted risk score showed similarshifts, with the majority of tumors typed high risk (68%) becomingintermediate or low risk in the on treatment samples. Tight correlationwith Ki67 immunohistochemistry further underscores this conclusion. Thecorrelation between baseline Ki67 values and PAM50 proliferationweighted risk score was high (P=2.8×E-8). Similarly the one month Ki67values and the one month PAM50 proliferation score were also tightlycorrelated (P=3.8E-10). However, while the baseline PAM50 proliferationweighted risk score subtype exhibited only a very weak correlation withthe end of study Ki67 values (P=0.04), there was a tight correlationbetween the one month PAM50 proliferation weighted risk score and theend of study Ki67 values—most of which were obtained at surgery 4 to 6months later (P=6.8E-11). This last observation strongly supports theclaim that an early on treatment PAM50 based test can be used to predictwhether the final surgical samples will have favorable biomarkerfeatures, such as a low proliferation rate.

To determine the clinical correlations associated with theseendocrine-therapy induced changes in intrinsic breast cancer subtype andrisk score, four endpoints were examined: clinical response (RECISTcriteria), pathological T size (T1 versus higher—as evidence forpathological down staging with treatment), dichotomized Ki67 values(with tumors exhibiting a Ki67 natural log value of 1 or less consideredto be exhibiting a favorable profile) and relapse events. The baselinesubtype or risk score showed no convincing ability to predict any ofthese endpoints, which, in terms of the relapse, is likely a function ofthe small sample size in this trial (Table 9). In contrast, and despitethe small sample size, the PAM50 intrinsic subtype at one month (Table10) did show statistically significant relationships with clinicalresponse (P=0.01), favorable end of treatment Ki67 value (P=0.0003) andrelapse (0.009). These strong relationships were driven by the extremelypoor outcome associated with tumors that were either designated“non-luminal” or Luminal B in the on treatment specimens. The PAM50proliferation-weighted risk score had similar properties. Baseline PAM50proliferation-weighted risks score did not predict the neoadjuvant orlong term outcomes very effectively (Table 11). However tumors that weredesignated high risk at one month showed significant correlations withpoor outcomes in all four endpoints examined, i.e., poor clinicalresponse (P=0.02), low pathological down-staging (p=0.02), unfavorableend of treatment Ki67 value (P=0.0001) and relapse (p=0.001) (Table 12).

Thus, application of the PAM50 based intrinsic subtype and risk score totumor samples harvested from primary ER+breast cancers undergoingpresurgical treatment with an endocrine agent can be used for thefollowing purposes:

-   -   1) Prediction of a failure to respond to neoadjuvant endocrine        therapy    -   2) Determination of the prognosis for patients with ER+breast        cancer subsequently undergoing adjuvant endocrine treatment.

TABLE 8 PAM50 subtype and proliferation-weighted risk group switching atone month after treatment. Change Category Number Percentage PAM50Intrinsic Subtype Changes LumA to LumA 18 31.0 LumA to LumB 1 1.7 LumAto Non-Lum 0 0 LumB to LumA 29 50.0 LumB to LumB 6 10.3 LumB to Non-Lum1 1.7 Non-Lum to Non-Lum 1 1.7 Non Lum to LumA 0 0 Non Lum to LumB 2 3.4Total 58 100 Proliferation weighted PAM50 Risk Score Low to Low 5 8.6Low to Med 1 1.7 Low to High 0 0 Med to Low 7 12.1 Med to Med 12 20.7Med to High 1 1.7 High to Low 11 19 High to Med 14 24.1 High to High 712.1 Total 58 100

TABLE 9 Interactions between the baseline PAM50 intrinsic subtypedesignations and outcomes from neoadjuvant endocrine therapy. Subtype orscore at End of Study % favorable P value on Baseline EndpointNumber/Total outcome interaction Subtype Clinical Response 0.54 CR + PRv SD + PD LumA 28/76 60.71 LumB 42/76 69.05 NonLum†  6/76 50.00 Pathtumor size* 0.29 ≤2 cm versus >2 cm LumA 29/78 37.79 LumB 43/78 48.84NonLum†  6/78 16.67 Log normal 0.03 Ki67# ≤ log 1.0 versus >1.0 LumA30/29 66.67 LumB 43/79 37.21 NonLum†  6/79 33.33 Relapse 0.262 Yesversus No LumA 30/78 90.00 LumB 42/78 90.4762 NonLum†  6/78 66.67 *Sinceall patients had clinical stage 2 or 3 disease, pathological tumor stageone are surgery was taken as evidence of successful down-staging. Tumorsthat progressed during therapy and underwent neoadjuvant chemotherapyare assumed to have a pathological T size of greater than 2 cm at theend of study. #End of study Ki67 is defined as either the surgicalspecimen or the one month value if the patient progressed on neoadjuvantendocrine therapy and underwent chemotherapy or did not undergo surgery.†Non-Luminal refers to samples designated Basal-like or HER2 enriched.Normal-like is not included in this analysis because these samples areassumed to not contain sufficient tumor cells for adequate subtyping.

TABLE 10 Interactions between one month on treatment PAM50 intrinsicsubtype designations and outcomes from neoadjuvant endocrine therapy.PAM50 Subtype at End of Study % favorable P value on one month EndpointNumber/Total outcome interaction Subtype Clinical Response 0.01 CR + PRv SD + PD LumA 45/56  75.56 LumB 9/56 44.44 NonLum 2/56 0 Path tumorsize* 0.41 ≤2 cm versus >2 cm LumA 46/57  47.83 LumB 9/57 22.22 NonLum2/57 50.00 Log normal 0.0003 Ki67# ≤ log 1.0 versus >1.0 LumA 47/58 61.70 LumB 9/58 0 NonLum 2/58 0 Relapse 0.009 Yes versus No LumA 45/53 93.62 LumB 7/53 57.14 NonLum 2/53 50.00 *Since all patients had clinicalstage 2 or 3 disease, pathological tumor stage one are surgery was takenas evidence of successful down-staging. Tumors that progressed duringtherapy and underwent neoadjuvant chemotherapy are assumed to have apathological T size of greater than 2 cm at the end of study. #End ofstudy Ki67 is defined as either the surgical specimen or the one monthvalue if the patient progressed on neoadjuvant endocrine therapy andunderwent chemotherapy or did not undergo surgery. †Non-Luminal refersto samples designated Basal-like or HER2 enriched. Normal-like is notincluded in this analysis because these samples are assumed to notcontain sufficient tumor cells for adequate subtyping.

TABLE 11 Interactions between baseline PAM50 proliferation weighted riskscore designations and outcomes from neoadjuvant endocrine therapy RiskScore, with proliferation at End of Study % favorable P value onBaseline Endpoint Number/Total outcome interaction† Clinical Response0.4573 CR + PR v SD + PD Low  9/76 44.44 Med 28/76 67.79 High 39/7666.67 Path tumor size* 1.0 ≤2 cm versus >2 cm Low  9/78 44.44 Med 29/7841.38 High 37/78 42.50 Log normal Ki67# 0.03431 ≤ log 1.0 versus >1.0Low  9/79 77.78 Med 30/79 56.67 High 40/79 35.00 Relapse 0.1191 Yesversus No Low  9/74 77.78 Med 29/74 96.67 High 36/74 84.62 *Since allpatients had clinical stage 2 or 3 disease, pathological tumor stage oneare surgery was taken as evidence of successful down-staging. Tumorsthat progressed during therapy and underwent neoadjuvant chemotherapyare assumed to have a pathological T size of greater than 2 cm at theend of study. #End of study Ki67 is defined as either the surgicalspecimen or the one month value if the patient progressed on neoadjuvantendocrine therapy and underwent chemotherapy or did not undergo surgery.†Non-Luminal refers to samples designated Basal-like or HER2 enriched.Normal-like is not included in this analysis because these samples areassumed to not contain sufficient tumor cells for adequate subtyping.

TABLE 12 Interactions between one month on therapy PAM50 proliferationweighted risk score and outcomes from neoadjuvant endocrine therapyPAM50 proliferation weighted risk score at one End of Study % favorableP value on month Endpoint Number/Total outcome interaction ClinicalResponse 0.02 CR + PR v SD + PD Low 21/56 80.95 Med 27/56 70.37 High 8/56 25.00 Path tumor size* 0.02 ≤2 cm versus >2 cm Low 23/57 47.83 Med26/57 53.85 High  8/57 0 Log normal 0.0001 Ki67# ≤ log 1.0 versus >1.0Low 23/58 78.26 Med 27/58 40.74 High  8/58 0 Relapse 0.001 Yes versus NoLow 23/56 95.65 Med 27/56 92.59 High  6/56 33.33 *Since all patients hadclinical stage 2 or 3 disease, pathological tumor stage one are surgerywas taken as evidence of successful down-staging. Tumors that progressedduring therapy and underwent neoadjuvant chemotherapy are assumed tohave a pathological T size of greater than 2 cm at the end of study.#End of study Ki67 is defined as either the surgical specimen or the onemonth value if the patient progressed on neoadjuvant endocrine therapyand underwent chemotherapy or did not undergo surgery. †Non-Luminalrefers to samples designated Basal-like or HER2-like, Normal-like is notincluded in this analysis because these samples are assumed to notcontain sufficient tumor cells for adequate subtyping.

Example 3

A risk of relapse analysis was performed on the samples described inExample 1, except the normal-like class was removed from the model. Thenormal-like class was represented using true “normals” from reductionmammoplasty or grossly uninvolved tissue. Thus, this class has beenremoved from the all outcome analyses and this classification isconsidered as a quality-control measure. Methods not described below areidentical to the methods described in Example 1.

Methods Prognostic and Predictive Models Using Clinical and MolecularSubtype Data:

Univariate and multivariate analyses were used to determine thesignificance of the intrinsic subtypes (LumA, LumB, HER2-enriched, andbasal-like) in untreated patients and in patients receiving neoadjuvantchemotherapy. For prognosis, subtypes were compared with standardclinical variables (T, N, ER status, and histological grade), with timeto relapse (i.e., any event) as the end point. Subtypes were comparedwith grade and molecular markers (ER, progesterone receptor (PR), HER2)for prediction in the neoadjuvant setting because pathologic staging isnot applicable. Likelihood ratio tests were done to compared models ofavailable clinical data, subtype data, and combined clinical andmolecular variables. Categoric survival analyses were performed using alog-rank test and visualized with Kaplan-Meier plots.

Developing Risk Models with Clinical and Molecular Data

The subtype risk model was trained with a multivariate Cox model usingRidge regression fit to the node-negative, untreated subset of the vande Vijver et al. (2002) cohort. A ROR score was assigned to each testcase using correlation to the subtype alone (1) (ROR-S) or using subtypecorrelation along with tumor size (2) (ROR-C):

ROR-S=0.05*Basal+0.12*Her2+−0.34*LumA+0.0.23*LumB  (1)

ROR-C=0.05*Basal+0.11*Her2+−0.23*LumA+0.09*LumB+0.17*T  (2)

The sum of the coefficients from the Cox model is the ROR score for eachpatient. The classify samples into specific risk groups, thresholds werechosen from the training set as described in Example 1. SiZer analysiswas performed to characterize the relationship between the ROR score andrelapse-free survival. The 95% CIs for the ROR score are local versionsof binomial CIs, with the local sample size computed from a Gaussiankernel density estimator based on the Sheather-Jones choice of windowwidth.

Comparison of Relapse Prediction Models

Four models were compared for prediction of relapse: (1) a model ofclinical variables alone (tumor size, grade, and ER status), (2) ROR-S,(3) ROR-C, and (4) a model combining subtype, tumor size, and grade. TheC-index was chose to compare the strength of the various models. Foreach model, the C-index was estimated from 100 randomizations of theuntreated cohort into two-thirds training set and one-thirds test set.The C-index was calculated for each test set to form the estimate ofeach model, and C-index estimates were compared across models using thetwo sample t test.

Results Risk of Relapse Models for Prognosis in Node-Negative BreastCancer

Cox models were tested using intrinsic subtype alone and together withclinical variables. Table 13 shows the multivariable analyses of thesemodels in an independent cohort of untreated patients (see Example 1).In model A, subtypes, tumor size (T1 or greater) and histologic gradewere found to be significant factors for ROR. The great majority ofbasal-like tumors (95.9%) were found to be medium or high grade, andtherefore, in model B, which is an analysis without grade, basal-likebecomes significant. Model C shows the significance of the subtypes inthe node-negative population. All models that included subtype andclinical variables were significantly better than either clinical alone(P<0.0001) or subtype alone (P<0.0001). A relapse classifier was trainedto predict outcomes within the context of the intrinsic subtypes andclinical variables. A node-negative, no systemic treatment cohort(n=141) was selected from the van de Vijver et al. (2002) microarraydata set to train the ROR model and to select cut-offs. There was aclear improvement in production with subtype (ROR-S) relative to themodel of available clinical variables only (see Parker et al. (2009) JClin Oncol 27(8):1160-1167). A combination of clinical variables andsubtype (ROR-C) is also a significant improvement over either individualpredictor. However, information on grade did not significantly improvethe C-index in the combined model, indicating that the prognostic valueof grade had been superseded by information provided by the intrinsicsubtype model. When using ROR-C for ROR in a prognostic test set ofuntreated node-negative patients, only the LumA group contained anylow-risk patients, and the three-class distinction of low, medium, andhigh risk was prognostic. Also, ROR-C scores have a linear relationshipwith probability of relapse at 5 years.

TABLE 13 Models of relapse-free survival (untreated) Model A Model BModel C Hazard Hazard Hazard Variable ratio P ratio P ratio P Basal-1.33 0.33 1.79 0.3 1.58 0.066 like* HER- 2.53 0.00012 3.25 <0.0001 2.9<0.0001 enriched* LumB* 2.43 <.0001 2.88 <0.0001 2.54 <0.0001 ER 0.830.38 0.83 0.34 0.83 0.32 Status† Tumor 1.36 0.034 1.43 0.012 1.57 0.001Size‡ Node 1.75 0.035 1.72 0.041 — — Status§ Histologic 1.4 0.0042 — — —— grade|| Full v. <.0001 <0.0001 <0.0001 subtype¶ Full v. <.0001 <0.0001<0.0001 clinical# *Luminal A class used as reference state inmultivariable analysis. †Hazard ratios for ER using positive marker inthe numerator. ‡Size ≤2 cm versus >2 cm. §Any positive node. ||Gradeencoded as an ordinal variable with three levels. ¶Significant P valuesindicate improved prediction relative to subtype alone. #Significant Pvalues indicate improved prediction relative to clinical data alone.

Subtypes and Prediction of Response to Neoadjuvant T/FAC Treatment

The Hess et al. (2006) study that performed microarray on tumors frompatients treated with T/FAC allowed investigation of the relationshipbetween the subtypes and clinical markers and how each relates to pCR>.Table 14 shows the multivariable analyses of the subtypes together withclinical molecular markers (ER. PR, HER2) and either with (model A) orwithout (model B) histologic grade. The only significant variables inthe context of this study were the intrinsic subtypes. A 94% sensitivityand 97% negative predictive value was found for identifyingnonresponders to chemotherapy when using the ROR-S model to predict pCR.The relationship between high-risk scores and a higher probability ofpCR is consistent with the conclusion that indolent ER-positive tumors(LumA) are less responsive to chemotherapy. However, unlike ROR forprognosis, a plateau seems to be reached for the ROR versus probabilityof pCR, confirming the presence of significant chemotherapy resistanceamong the highest risk tumors.

TABLE 14 Models of neoadjuvant response Model A Model B Model C OddsOdds Odds Variable ratio P ratio P ratio P Basal- 1.33 0.33 1.79 0.31.58 0.066 like* HER- 2.53 0.00012 3.25 <0.0001 2.9 <0.0001 enriched*LumB* 2.43 <.0001 2.88 <0.0001 2.54 <0.0001 ER 0.83 0.38 0.83 0.34 0.830.32 Status† PR Status† 1.36 0.034 1.43 0.012 1.57 0.001 Histologic 1.40.0042 — — — — grade‡ Full v. <.0001 <0.0001 <0.0001 subtype§ Full v.<.0001 <0.0001 <0.0001 clinical|| *Luminal A class used as referencestate in multivariable analysis. †Hazard ratios for ER, PR and HER2 arepositive marker in the numerator. ‡Grade encoded as an ordinal variablewith three levels. §Significant P values indicate improved predictionrelative to subtype alone. ||Significant P values indicate improvedprediction relative to clinical data alone.

Example 4

In this study, qRT-PCR and previously established cut points (seeExample 1) was used to assess the prognostic value of the PAM50classifier in the common, clinically-important group of women who areestrogen receptor positive and treated with tamoxifen as their soleadjuvant systemic therapy. Unlike in most previous reports, thishomogeneously-treated study cohort includes a large proportion of lymphnode positive patients. The available detailed long term follow-uppermits assessment not only of relapse-free survival, but also of therisk of breast cancer disease-specific death, in comparison with allstandard clinicopathologic risk factors.

Methods Patients:

The study cohort is derived from female patients with invasive breastcancer, newly diagnosed in the province of British Columbia in theperiod between 1986 and 1992. Tissue had been excised at varioushospitals around the province, frozen and shipped to the centralestrogen receptor (ER) laboratory at Vancouver Hospital; the portion ofthe received material that was formalin-fixed and paraffin-embedded as ahistological reference is used in this study. Clinical informationlinked to the specimens includes age, histology, grade, tumor size,number involved axillary nodes, lymphatic or vascular invasion, ERstatus by the DCC method, type of local and initial adjuvant systemictherapy, dates of diagnosis, first local, regional or distantrecurrence, date and cause of death. Characteristics of this patientcohort have been previously described in detail in a population-basedstudy validating the prognostic model ADJUVANT! [Olivotto 2005], and thesame source blocks were used to assemble tissue microarrays that havebeen characterized for ER [Cheang 2006] and HER2[Chia 2008] expression.For this study, patients were selected who had ER positive tumors byimmunohistochemistry, and received tamoxifen as their sole adjuvantsystemic therapy. During the time period when these patients receivedtheir treatment, provincial guidelines recommended adjuvant tamoxifenfor post-menopausal women, with ER-positive tumors who had some highrisk features present such as lymphovascular invasion. Similar patientswithout high risk features were mainly treated without adjuvant systemictherapy. In most cases, chemotherapy was only offered to premenopausalwomen.

RNA Preparation:

RNA was isolated from pathologist-guided tissue cores. Briefly, H&Esections from each block were reviewed by a pathologist. Areascontaining representative invasive breast carcinoma were selected andcircled on the source block. Using a 1.0 mm punch needle, at least twotumor cores were extracted from the circled area. RNA was recoveredusing the High Pure RNA Paraffin Kit (Roche Applied Science,Indianapolis Ind.), DNA removed with Turbo Dnase (Ambion, Austin Tex.),and RNA yield assessed using an ND-1000 Spectrophotometer (NanopropTechnologies, Rockland Del.).

qRT-PCR:

cDNA synthesis was done using a mixture of random hexamers andgene-specific primers, and qPCR was performed with the Roche LightCycler480 instrument as previously described [Mullins 2007]. Each 384-wellplate contained samples in duplicate (2.5 ng cDNA per reaction) and acalibrator in triplicate (10 ng cDNA per reaction). A tumor sample wasconsidered of insufficient quality if any of the reference controls(ACTB, PSMC4, RPLP0, MRPL19, or SF3A1) failed. PCR was technicallysuccessful for all 50 discriminator genes in 73% of cases, and for 49 ofthe 50 in another 15% of cases. To assess the tolerance of the PAM50assay results to missing gene information, ROR-C values were assessed inthe data following random simulated removal of an increasing number ofgenes. Loss of one gene resulted in a 0-2 unit change in risk score,corresponding to a 1% increase/decrease in disease-specific survival at10 years.

Assignment of Biological Subtype to Clinical Samples:

Gene expression centroids corresponding to Luminal A, Luminal B,HER2-enriched, Basal-like and Normal-like subtypes were constructedusing the intrinsic 50 gene panel as described in Example 1 and inParker et al. (2007 J. Clin. Oncol. 27(8):1160-7, which is hereinincorporated by reference in its entirety). Specimens were assigned toan intrinsic subtype based on the nearest centroid distance calculatedby Spearman's rank correlation, by investigators blinded to outcomedata.

Relation of PAM50 Subtype to Clinical Outcome:

Statistical analyses were conducted using SPSS v16.0 and R v2.8.0.Univariate analysis of tumor subtype against breast cancer distantrelapse-free and breast cancer disease-specific survival was performedby Kaplan-Meier analysis, with log rank test for significance.Multivariate analysis was performed against the standard clinicalparameters of tumor size, nodal status (% positive nodes over totalexamined), histologic grade, patient age and HER2 status (based onadjacent cores from the same source block, assembled into tissuemicroarrays and subjected to immunostaining and FISH analysis usingclinical-equivalent protocols [Chia 2008]). Cox regression models [Cox1984] were built to estimate the adjusted hazard ratios of theqPCR-assigned breast cancer subtypes[Truong 2005]. Only cases withinformation for all the covariates were included in the analysis.Smoothed plots of weighted Schoenfeld residuals were used to assessproportional hazard assumptions [Grambsch 1994].

Relation of Risk-of-Relapse (ROR) Score to Clinical Outcome:

The ROR score algorithm (ROR-S incorporating a sample's correlation tothe Luminal A, Luminal B, HER2-enriched, and Basal-like subtypes; ROR-Cincorporating this information plus tumor size) was trained andvalidated on three microarray-profiled and one qPCR-profiled breastcancer series. Risk stratification cutpoints were assigned in thetraining set such that no Luminal A patients fell into the high riskcategory, and no Basal-like patients fell into the low risk category.Kaplan-Meier and Cox regression analyses were conducted as above.

Results

From surgical specimens which had been formalin-fixed and paraffinembedded 15-20 years previously, tumor cores were extracted frompathologist-identified areas of invasive breast carcinoma for 991 cases.Following RNA extraction, 815 samples yielded at least 1.2 μg total RNAat a concentration of at least 25 ng/μL, and proceeded to PCR analysis.Template was of technically sufficient quality (based on internalhousekeeper gene controls) for qRT-PCR in 806. Among these cases, atotal of 711 specimens yielded high quality qRT-PCR quantitative datafor at least 49 of the PAM50 discriminator genes, and were included insubsequent clinical and survival analyses. Clinical characteristics forthese 711 patients are presented in Table 15.

TABLE 15 Whole TAM Clinical Parameter series Luminal A Luminal B Her2Basal Normal Sample N 711 329 312 58 3 9 Size Age (in Median [IQR] 67 6768 66 65 66 years) Pre- Yes 18 9 7 2 0 0 menopausal No 678 315 297 56 37 Unknown/Pregnant 15 5 8 0 0 2 Surgery Complete 428 187 196 36 3 6Mastectomy Partial 274 139 111 21 0 3 Mastectomy Other 9 3 5 1 0 0Axillary Yes 675 308 298 57 3 9 Node No 36 21 14 1 0 0 DissectionBreast/chest Yes 372 180 153 34 0 5 wall No 339 149 159 24 3 4 radiationtherapy Adjuvant Yes 711 329 312 58 3 9 Tamoxifen No 0 0 0 0 0 0Adjuvant Yes 0 0 0 0 0 0 Chemotherapy No 711 329 312 58 3 9 Tumor SizeMedian [IQR] 2.2 2.0 2.5 2.5 2.5 3.0 (cm) T Stage T0/IS 0 0 0 0 0 0(Clinical) T1 298 155 113 24 3 3 T2 346 147 169 27 0 3 T3 18 10 5 3 0 0T4 28 9 15 1 0 3 TX 21 8 10 3 0 0 #Positive 0 199 83 91 18 0 7 Nodes 1-3328 162 139 24 1 2 4-9 111 49 51 10 1 0 10+ 26 8 16 2 0 0 Unknown 47 2715 4 1 0 Grade Grade 1: well 24 20 2 1 0 1 differentiated Grade 2: 306169 119 13 0 5 moderately differentiated Grade 3: poorly 338 117 173 432 3 differentiated Unknown 43 23 18 1 1 0 histologic ductal NOS 642 289288 54 3 8 subtype lobular 54 30 19 4 0 1 mucinous 7 4 3 0 0 0 tubular 55 0 0 0 0 medullary 2 1 1 0 0 0 apocrine 1 0 1 0 0 0 Lymphovascular Yes444 184 215 39 1 5 invasion No 230 122 84 18 2 4 Unknown 37 23 13 1 0 0Clinical missing 6 4 2 0 0 0 estrogen negative (0-9 fmol/mg) 9 3 2 4 0 0receptor status Positive (>10 fmol/mg) 696 322 308 54 3 9 (DCC)Immunohisto- negative 0 0 0 0 0 0 chemical ER positive 711 329 312 58 39

Based on the nearest PAM50 centroid, a total of 329 (46.3%) of theseclinically ER positive cases were assigned as Luminal A, 312 (43.8%) asLuminal B, 58 (8.2%) as HER2-enriched, 3 (0.4%) as Basal-like, and 9(1.3%) as Normal-like intrinsic breast cancer subtypes by geneexpression (Table 13). For the nine cases assigned as Normal-like, thehistology was reviewed, using the tissue microarray cores taken from thesame area of the source block. In eight of these nine cases, viableinvasive cancer cells were absent or rare in an immediately adjacentcore, consistent with the normal-like expression profile representing aninadequate tumor sampling. Normal-like cases were therefore excludedfrom further analysis.

Intrinsic biological subtype was strongly prognostic by Kaplan-Meieranalysis (FIGS. 4A and 4B). In the British Columbia population at thetime of sample acquisition for this study, many patients with aclinically low risk profile received no adjuvant systemictherapy[Olivotto 2005]. In contrast, those receiving adjuvant tamoxifenwho are the subjects in this study comprised a higher clinical riskgroup, with overall 10 year distant relapse-free survival rates of 62%and breast cancer disease-specific survival rates of 72%. Thosedetermined by the PAM50 assay to have a Luminal A profile had asignificantly better outcome (10 year relapse free survival 74%,disease-specific survival=83%) than Luminal B, HER2-enriched or basallike tumors.

All cases in this study were positive for estrogen receptor bycentrally-assessed immunohistochemistry [Cheang 2006], and 98.7% werealso positive by clinical dextran-charcoal coated biochemical assay.Despite this, the PAM50 qPCR panel assigned 10% of cases to non-luminalsubtypes, mostly HER2-enriched, as was previously observed wheninterrogating published datasets for expression of the PAM50 genes(Example 2).

For this cohort of clinically estrogen receptor positive women,uniformly treated with tamoxifen as their sole adjuvant systemictherapy, a multivariable Cox model was constructed to test theindependent value of PAM50 subtype against patient age and the standardclinicopathologic factors of tumor size, nodal status, histologic gradeand HER2 expression (Table 16). Intrinsic biological subtype remainedsignificant in the multivariable model, as were nodal status and tumorsize, but grade and clinical HER2 status, significant in univariateanalysis in this cohort, did not contribute significant independentprognostic information for either relapse-free or disease-specificsurvival in the multivariate model incorporating the PAM50 result.

TABLE 16 Cox model univariate and multivariate analyses incorporatingPAM50 biological subtype for relapse-free and breast cancerdisease-specific survival among (A) 604 women with ER positive,tamoxifen-treated breast cancer with complete data for all covariatesfor relapse-free survival, and (B) breast cancer disease-specificsurvival (BCDSS; excludes 2 cases with unknown cause of death). Clinicalunivariate relapse-free survival multivariate relapse-free survivalendpoint hazard ratio (95% CI) p-value hazard ratio (95% CI) p-value age 1.00 (0.990-1.02) 0.53 0.996 (0.981-1.01) 0.62 (continuous) grade (1 or1.45 (1.12-1.89) 0.0047  1.11 (0.846-1.46) 0.45 percent nodes positive 0vs. (>0 to 1.66 (1.15-2.39) 0.0070 1.76 (1.22-2.55) 0.0028 <25%) 0 vs.≥25% 2.98 (2.10-4.22) 7.3E−10 2.85 (2.00-4.06) 6.3E−9 tumor size 2.02(1.55-2.65) 2.5E−7 1.71 (1.30-2.24) 1.3E−4 ≤2 cm vs. >2 cm HER2 1.52(1.04-2.23) 0.032  1.24 (0.813-1.88) 0.32 (IHC) {0, 1 or 2+ FISHnegative} vs. {2+ FISH positive, or 3+} PAM50 subtype Luminal A 1.73(1.31-2.28) 1.0E−4 1.62 (1.22-2.16) 9.2E−4 vs. Luminal B Luminal A 1.86(1.18-2.92) 0.0074  1.53 (0.929-2.52) 0.095 vs. Her2- Enriched Luminal A76.4 (9.79-597) 3.5E−5 62.5 (7.87-496) 9.2E−5 vs. Basal- like B.Clinical Univariate BCDSS Multivariate BCDSS endpoint hazard ratio (95%CI) p-value hazard ratio (95% CI) p-value age  1.02 (0.999-1.03) 0.069 1.01 (0.988-1.02) 0.56 (continuous) grade (1 or 2) 1.43 (1.07-1.91)0.015  1.05 (0.988-1.02) 0.76 vs. 3 percent nodes positive 0 vs. (>0 to1.56 (1.03-2.37) 0.034 1.68 (1.11-2.56) 0.015 <25%) 0 vs. ≥25% 3.22(2.19-4.73) 2.4E−9 3.04 (2.06-4.48) 2.3E−8 tumor size 2.29 (1.96-3.10)8.0E−8 1.90 (1.40-2.58) 4.3E−8 ≤2 cm vs. >2 cm HER2 (IHC) 1.54(1.01-2.35) 0.043  1.19 (0.755-1.86) 0.46 {0, 1 or 2+ FISH negative} vs.{2+ FISH positive, or 3+} PAM50 subtype Luminal A vs. 2.05 (1.50-2.80)6.0E−6 1.90 (1.37-2.62) 1.0E−4 Luminal B Luminal A vs.  2.2 (1.33-3.64)0.0021 1.85 (1.07-3.20) 0.028 Her2-Enriched Luminal A vs. 104 (13.1-832)1.2E−5 9 1.1 (11.2-743) 2.5E−5 Basal-like

A risk-of-relapse (ROR) score can be calculated from the PAM50 qPCRpanel. Both the ROR-S (based only on molecular subtyping from the PAM50panel) and ROR-C (combining subtype and tumor size information) scoresare highly prognostic in a population homogeneously treated withadjuvant tamoxifen, to a series containing large numbers of nodepositive cases, and to the endpoint of breast cancer-specific survival(FIGS. 5A and 5B).

As shown in FIGS. 6A and 6B, the ROR-C algorithm is not only highlyprognostic among node negative patients, but reveals even widerdifferences in disease-specific survival among node positive patients.The algorithm identifies 16% of clinically ER positive patients (treatedwith adjuvant tamoxifen but not chemotherapy) who, despite being nodepositive, are classed as low risk, and these women have a 10 yeardisease-specific survival rate of 89%.

As a continuous variable, ROR-C has a significant interaction withpercentage of positive lymph nodes, and borderline significantinteraction with nodal stage (Table 17). Nodal stage is a significantpredictor among patients with moderate to high ROR-C values (>23.5), butamong patients with low ROR-C scores, outcomes are good regardless ofnodal status (FIGS. 7A to 7D and FIG. 8).

TABLE 17 Interaction test between PAM50- and tumor size-derived ROR-Cscore, expressed as a continuous variable, and axillary lymph nodestatus (A) expressed as % positive nodes or (B) categorized by nodalstage (where referrent group is node negative, N cat2 = 1-3 involvedaxillary nodes, and N cat3 = 4 or more involved axillary nodes). Themodel in Table 17A uses the proportion of positive nodes and theinteraction is significant. The model in Table 17B uses 3 level nodestatus (N-, 1-3 pos, >3 pos) and interaction is borderline. Only maineffects Interaction Variable Hazard p-value Hazard p-value Table 17AROR-C 1.75 1.60E−11 1.73 8.8E−11 Pos Node % 1.56 2.50E−10 1.43 0.000017Interaction 1.17 0.043 Full vs red 0.04 Table 17B ROR-C 1.77 6.20E−111.52 0.018 N cat2 1.8 9.40E−03 1.73 0.022 N cat3 3.88 1.20E−08 3.151.40E−05 ROR*N cat2 1.08 0.71 ROR*N cat3 1.62 0.061 Full vs red 0.11

As ROR-C includes tumor size information, to assess if the ROR algorithmgives independent additional prognostic information beyond standardclinical parameters (including tumor size) in this patient population,Cox models incorporating ROR-S were tested (Table 18). Regardless ofwhether the endpoint is relapse-free or disease-specific survival, or ifROR-S is included as a categorical or as a continuous variable, itremains significant, whereas grade and clinical HER2 status are notsignificant in multivariate analyses that include the qPCR-derivedinformation.

TABLE 18 Cox model multivariate analysis incorporating ROR-S score forbreast cancer disease-specific survival among women with ER positive,tamoxifen-treated breast cancer and complete data for all covariates.(A) ROR-S-defined risk categories, using prespecified cutpoints. (B)ROR-S as a continuous variable. Clinical relapse-free survival (N = 613)disease-specific survival (N = 611) endpoint hazard ratio (95% C.I.)p-value hazard ratio (95% C.I.) p-value A. age 0.995 (0.980-1.01) 0.56 1.00 (0.988-1.02) 0.56 (continuous) grade (1 or  1.03 (0.785-1.36) 0.81 1.00 (0.738-1.36) 1.0 2) vs. 3 percent nodes positive 0 vs. (>0 to 1.79(1.24-2.58) 0.0016 1.74 (1.16-2.63) 0.0081 <25%) 0 vs. ≥25% 2.87(2.02-4.08) 4.4E−9 3.10 (2.10-4.57) 1.3E−8 tumor size ≤2 cm 1.70(1.30-2.23) 1.2E−4 1.92 (1.42-2.61) 2.8E−5 vs. >2 cm HER2 (IHC)  1.14(0.760-1.72) 0.52  1.10 (0.701-1.74) 0.67 {0, 1 or 2+ FISH negative} vs.{2+ FISH positive, or 3+} ROR-S (categorized) low vs. 2.00 (1.39-2.87)1.9E−4 2.21 (1.45-3.36) 2.1E−4 medium low vs. high 2.68 (1.63-4.41)1.0E−4 3.25 (1.86-5.67) 3.4E−5 B. age 0.997 (0.982-1.01) 0.71  1.01(0.989-1.02) 0.48 (continuous) grade (1 or  1.06 (0.808-1.40) 0.66  1.02(0.749-1.38) 0.92 2) vs. 3 percent nodes positive 0 vs. (>0 to 1.77(1.23-2.53) 0.0021 1.71 (1.13-2.58) 0.011 <25%) 0 vs. ≥25% 2.87(2.02-4.06) 3.4E−9 3.12 (2.12-4.59) 8.5E−9 tumor size ≤2 cm 1.70(1.30-2.23) 1.2E−4 1.92 (1.41-2.60) 3.0E−5 vs. >2 cm HER2 (IHC)  1.05(0.699-1.59) 0.80 0.986 (0.628-1.55) 0.95 {0, 1 or 2+ FISH negative} vs.{2+ FISH positive, or 3+} ROR-S 1.02 (1.01-1.03) 7.3E−5 1.02 (1.01-1.03)1.0E−5 (continuous)

The cases in this series have previously been assessed byimmunohistochemistry for ER, PR, HER2, cytokeratin 5/6, epidermal growthfactor receptor, and Ki67 [Cheang 2008] [Cheang 2009], allowingintrinsic subtyping to be assigned by a surrogate immunohistochemicaldefinition. As all cases in this series are ER positive byimmunohistochemistry, all were assigned as either Luminal A (if HER2negative and Ki67 low) or Luminal B (if HER2 positive or Ki67 high). Theavailability of qPCR subtyping assignments allows a comparison withimmunohistochemical assignment on the same material, against patientoutcome in this homogeneously-treated cohort. A total of 606 cases hadsufficiently complete immunohistochemical and qPCR data for assignmentto a Luminal subtype by both methods. Among these, 255 were assigned asLuminal A and 193 as Luminal B by both methods, whereas 99 were assignedLuminal A by immunostain but Luminal B by qPCR, and 59 as Luminal B byimmunostain but Luminal A by qPCR, for a concordance of 74%, kappa=0.48.Where the results were discordant, only the cases assigned as Luminal Bby PCR had significantly poorer outcome than those concordantly assignedas Luminal A. In multivariable analysis among these cases, bothimmunohistochemical and PAM50 assignment are independently significantpredictors for relapse-free survival, whereas grade and HER2 status fallout of the model (Table 19). For disease-specific survival, PAM50 issignificant whereas immunohistochemistry is borderline. The magnitude ofthe identified hazard is higher with the qPCR assignment for bothendpoints. In a step-wise Cox regression model incorporating bothimmunohistochemical and qPCR assignment, only qPCR stays significant.

TABLE 19 Cox model multivariate analyses for Luminal cases, comparingthe prognostic information from intrinsic subtyping byimmunohistochemistry versus PAM50 qPCR. immunohistochemical subtypePAM50 qPCR subtype Clinical endpoint hazard ratio (95% CI) p-valuehazard ratio (95% CI) p-value A. Relapse-free survival (N = 606) age(continuous) 0.992 (0.98-1.01)  0.36 0.990 (0.97-1.01)  0.26 grade (1 or2) vs. 3 1.18 (0.89-1.57) 0.24 1.12 (0.84-1.49) 0.43 percent positivenodes 0 vs. (>0 to <25% 1.66 (1.11-2.48) 0.014 1.68 (1.12-2.50) 0.012 0vs. ≥25% 2.86 (1.95-4.19) 7.2E−8 2.93 (2.00-4.30) 3.8E−8 tumor size ≤2cm 1.80 (1.34-2.42) 8.6E−5 1.81 (1.35-2.42) 7.4E−5 vs. >2 cm HER2 (IHC)1.21 (0.74-1.99) 0.45 1.30 (0.81-2.09) 0.27 {0, 1, or 2+ FISH negative}vs. {2+ FISH positive or 3+} Luminal B vs. 1.38 (1.02-1.86) 0.035 1.61(1.20-2.16) 0.0014 Luminal A B. Breast cancer disease-specific survival(N = 605; excludes one death of uncertain cause) age (continuous) 1.00(0.98-1.02) 0.67 1.00 (0.98-1.02) 0.89 grade (1 or 2) vs. 3 1.14(0.83-1.55) 0.42 1.05 (0.77-1.44) 0.74 percent positive nodes 0 vs. (>0to <25% 1.44 (0.92-2.26) 0.106 1.50 (0.96-2.34) 0.077 0 vs. ≥25% 2.79(1.84-4.23) 1.2E−6 2.88 (1.90-4.38) 5.8E−7 tumor size ≤2 cm 2.07(1.48-2.89) 1.8E−5 2.06 (1.48-2.87) 1.7E−5 vs. >2 cm HER2 (IHC) 1.27(0.75-2.15) 0.38 1.29 (0.78-2.13) 0.32 {0, 1, or 2+ FISH negative} vs.{2+ FISH positive or 3+} Luminal B vs. 1.38 (0.99-1.93) 0.060 1.89(1.36-2.62) 1.5E−4 Luminal AResults from Adjuvant! Predictions

A comparison of the outcome predicted by the Adjuvant! model withoutcome predicted by the ROR model was made in a cohort of breast cancerpatients. This cohort consists of 806 patients diagnosed with invasive,estrogen receptor positive breast cancer, between the dates of 1986 and1992. All patients had primary surgery and adjuvant systemic therapywith tamoxifen alone; none of these patients were treated withchemotherapy. The Adjuvant prognostic model was used to calculate theprobability of breast cancer specific survival (BCSS) at 10 years usingthe standard clinicopathological features of patient age, tumor size,histological grade, lymphovascular invasion, and number of positivelymph nodes. All patients were ER positive, and the risk of breastcancer death was adjusted for adjuvant tamoxifen therapy.

Of the 806 patients, 748 had sufficient clinicopathological data toobtain an Adjuvant estimate of BCSS. The remaining 58 patients hadeither missing tumor size or missing lymph node data. The mean Adjuvantpredicted BCSS was 73.7%. This corresponds to the observed BCSS of73.2%. The cohort was then divided into subgroups based on the Adjuvantpredicted BCSS at 10 years (Table 20).

TABLE 20 Adjuvant! Predicted Risk Category 10-year BCSS N Number ofEvents 1  90-100% 122 16 2 80-90% 164 32 3 70-80% 168 60 4 <70% 292 121

The observed BCSS at 10 years, for each of the Adjuvant risk groups wassimilar to the BCSS predicted by Adjuvant (Table 21). One notableexception is the lowest risk group, in subgroup with an Adjuvant!predicted BCSS of 90-100%, the observed BCSS at 10 years is 89%.Consequently, it appears that Adjuvant is overestimating survival isthis low risk group. This is consistent with the validation study ofAdjuvant! using the BCOU database (Olivotto et al. (2005)). In thisvalidation study, it was found that Adjuvant underestimated breastcancer deaths by 4.9% in the subgroup with T1N0 disease. In the subgroupof patients with a predicted BCSS of 90-100%, 87 of 122 patients hadT1N0 breast cancer.

TABLE 21 Mean Adjuvant Adjuvant Risk Predicted BCSS at Observed BCSS atGroup 10 years 10 years  90-100% 94% 89% 80-90% 85% 83% 70-80% 76% 75%<70% 58% 61%

ROR-S using qRT-PCR data from 50 genes was then used to separate eachAdjuvant! group into low vs. medium/high risk (Table 22). Due to therelatively small size of each group, medium and high risk groups werecombined to improve statistical power. Also, because the Adjuvant! risksubgroups are already defined using clinical factors, ROR-S (rather thanROR-C) was applied.

TABLE 22 Adjuvant Risk ROR-S Medium or Group ROR-S Low High  90-100% 4775 80-90% 56 108 70-80% 43 125 <70% 58 234

Kaplan-Meier analysis was then performed separately on each Adjuvantrisk group, and differences in survival between the low vs. med/highrisk ROR-S groups were tested using the log-rank test (Table 23). It wasobserved that ROR-S could isolate a low risk subgroup in each of theAdjuvant Risk Groups. Statistically significant differences in BCSS werefound for low risk vs. medium/high risk patients, in all subgroupsexcept for the 90-100% group.

TABLE 23 BCSS for BCSS for Adjuvant Observed Low Risk Med/High RiskLog-rank Test Risk Group BCSS ROR-S ROR-S of ROR-S  90-100% 89% 93% 85%p = 0.058 80-90% 83% 92% 78% p = 0.020 70-80% 75% 95% 68% p = 0.005 <70%61% 71% 58% P = 0.009

In this low risk group, ROR-S is not quite statistically significant(p=0.058). However, this group does provide some convincing evidencethat the ROR-S is adding additional prognostic information to Adjuvant!.A Kaplan-Meier analysis of the same group but with Adjuvant! predictedBCSS 90-95% vs. 95-100% is shown in FIG. 9.

In the intermediate risk groups, ROR-S performs well in identifying lowrisk vs. higher risk patients. In both the 80-90% group (FIG. 10A) and70-80% group (FIG. 10B), the ROR-S identifies subgroups with 10 yearBCSS>90%. This is an important result as ROR-S identifies traditionallyhigh-risk patients that do well without chemotherapy.

In the very high risk subgroup identified by Adjuvant! (PredictedBCSS<70%), ROR-S is still able to identify distinct prognostic groups(FIG. 10C).

Discussion

Previous studies have established that intrinsic biological signaturescharacteristic of Luminal A, Luminal B, HER2-Enriched and Basal-likesubtypes are present and have prognostic significance in breast cancercohorts from multiple different institutions, profiled with several geneexpression microarray platforms [Calza 2006] [Kapp 2006][Hu 2006][Fan2006]. In order to identify these subtypes on standard formalin-fixed,paraffin-embedded pathology specimens, a quantitativereverse-transcriptase PCR test [Mullins 2007] was developed thatidentifies these subtypes based on a panel of 50 genes.

The analysis reported here consists exclusively of qPCR-based testing,applied to a series of relatively old-age (15-20 years) paraffin blockswith long and detailed follow-up, allowing analysis not only ofrelapse-free survival, but also of breast cancer disease-specificsurvival. The present study consists of women with estrogen receptorpositive breast cancer who received hormonal therapy (tamoxifen) astheir sole adjuvant treatment, a group of particular clinical importanceand contemporary relevance. Estrogen receptor and HER2 status werecentrally determined. 70% of these women were node positive atpresentation, and in current practice would usually be recommended toreceive adjuvant chemotherapy. The PAM50 subtype assignment asdetermined by PCR is highly prognostic in these women. Subtype remainssignificant in multivariate analysis, whereas grade and clinical HER2status do not. Findings using the commonly-employed surrogate endpointof relapse-free survival all hold for breast cancer disease-specificsurvival.

Although the patients from this cohort were treated more than 20 yearsago, the findings from this study remain relevant to the treatment ofbreast cancer patients with a moderate risk of relapse. Such patientsmay derive significant benefit from adjuvant hormonal therapy but thefurther addition of chemotherapy may have modest effects (2-5%improvement in 10-year relapse free survival). While the decision topursue adjuvant chemotherapy is an individual decision made by thepatient and consulting oncologist, improved prognostication willfacilitate therapeutic decision making.

A Risk of Relapse score was developed and validated on microarray datafrom node negative patients who received no adjuvant systemic therapy(Example 2), against the endpoint of relapse-free survival. Thisalgorithm is shown to predict pathologic complete response in apublished neoadjuvant T/FAC clinical trial dataset of 133 patients, and,in its qPCR format, to predict relapse-free survival in a cohort of 279heterogeneously-treated women with breast cancer. ROR scores generatedby qPCR from paraffin block specimens are also prognostic intamoxifen-treated, estrogen positive women, in both node-negative andnode-positive subsets. ROR-C identifies a group of low risk patientsamong whom even nodal status is not a predictor, and who might thereforenot require treatment approaches usually reserved for node positivepatients including, for example, third generation chemotherapy regimensand chest wall radiation.

Very few cases (1.3%) are classified as Normal-like using the PCR assay,as compared to 12% when the PAM50 classifier is applied to DNAmicroarray data from large sets of primary breast cancers. DNAmicroarray analyses utilize homogenized tumor specimens that, despitegross dissection to enrich for tumor, may still contain significantamounts of normal breast tissue. In contrast, the PAM50 qPCR assay isperformed on a pathologist-guided tissue core, based on directmicroscopic identification of a representative area of pure tumor in thesource block. This difference likely accounts for the much lowerfrequency of Normal-like profiles obtained using the PAM50 qPCR methodapplied to paraffin blocks. Review of the histology, as represented ontissue microarray cores extracted from the immediately adjacent tissue,is consistent with inadequate tumor representation being responsible fora normal-like profile in eight of the nine normal-like cases.

As was previously noted based on interrogation of published datasetswith the PAM50 classifier, this assay identifies ER-negative biologicalsubtypes among clinically ER positive women even in a setting where thetumor is positive by both immunohistochemical and ligand-binding assays.Fully 10% of cases are re-assigned to non-luminal subtypes, and thesetamoxifen-treated women had poor outcomes, compatible with a biologicalreality of hormone independence. Clinical measurements of ER and HER2status, on their own, can stratify breast cancer patients intoprognostic and predictive subgroups[Hayes 2007]. Nevertheless, relyingon measurements of single genes (ER, PR) to assign breast cancerprognosis and treatment risks not only the problems of false positiveand negative single measurements, but also the possibility that atumor's underlying biology may be hormone independent (despite onemember of the pathway being expressed at the protein level). In thisrespect, the information provided by concurrently measuring 50 genes,including others in the estrogen response pathway together with positivemarkers of other biological subtypes, is likely to be a more accuratereflection of the underlying tumor biology [Oh 2006].

Larger immunohistochemical surrogate panels have been linked toexpression profile gold standards and can provide more information thansimple measurement of ER, PR and HER2 [Cheang 2008b][Cheang 2009].Limited antibody panels are easily applied to standard paraffin blocks,and can add significant prognostic information beyond standardclinicopathologic risk factors [Ross 2008]. In this study, a directcomparison of an established six immunostain panel (ER, PR, HER2, Ki67,cytokeratin 5/6 and epidermal growth factor receptor) against the 50gene qPCR assay, was made using the same source blocks. Each method addssignificant prognostic information beyond standard factors. However, inthis set of clinically ER positive patients, there were many discrepantassignments to an intrinsic biological subtype, and the qPCR approachwas better at predicting outcome in these cases.

In multivariate analysis incorporating the main clinical risk factors,grade is no longer significant when PAM50 subtype or ROR is included. Incomparison with other signatures such as the recurrence score andgenomic grade indexes [Paik 2004] [Ivshina 2006] [Sotiriou 2006], thePAM50 also has the advantage of discriminating high risk cases intoLuminal B, HER2-Enriched and Basal-like subtypes, who are likely torespond differently to systemic therapy options (for example, hormonal,anti-HER2, and anthracycline vs. non-anthracycline chemotherapyregimens). The assay is also easier to perform, as it does not requirefrozen tissue [Glas 2006] nor manual microdissection of cut sections[Paik 2004] and can be readily applied to standard paraffin blocksincluding archival tissues such as those from clinical trials. However,the assay can be performed on these types of samples if desired. Becausethe PAM50 assay was designed to reflect the major features of theunderlying biology of breast cancer, as opposed to being optimizedagainst outcome in a particular population, it is particularly likely toextrapolate well onto other patient cohorts, and remain predictive[Rouzier 2005]. In this study, it was demonstrated for the first timethat the PAM50 qPCR assay has significant and independent prognosticcapacity among estrogen receptor positive, tamoxifen treated women,whether node positive or node negative. The assay identifies up to 10%cases that were clinically determined to be ER positive (byimmunohistochemistry and ligand-binding assay) as falling into ERnegative high-risk groups, replaces grade and HER2 status inmultivariate prognostic models, and is superior to immunohistochemicalsubtyping and clinical risk classifiers.

REFERENCES

-   Calza et al. (2006) Breast Cancer Res. 8:R34.-   Cheang et al. (2006) J Clin Oncol. December 20; 24(36):5637-44.-   Cheang et al. (2008a) Annu Rev Pathol. 3:67-97.-   Cheang M C et al. (2008b) Clin Cancer Res. 14(5):1368-76.-   Cheang M C et al. (2009) J Natl Cancer Inst. 101(10):736-50.-   Chia S et al. (2008) J Clin Oncol. 26(35):5697-704.-   Cox and Oakes (1984) Analysis of Survival Data. Chapman & Hall    (London, England).-   Cronin M et al. (2007) Clin Chem 53:1084-91.-   Fan et al. (2006) N. Engl. J. Med. 355:560-69.-   Glas et al. (2006) BMC Genomics 7:278.-   Grambsch and Therneau (1994) Biometrika 81(3):515-26.-   Hayes et al. (2007) N Engl J. Med. 357(15):1496-506.-   Hu et al. (2006) BMC Genomics 7:96.-   Kapp et al. (2006) BMC Genomics 7:231.-   Loi et al. (2007) J Clin Oncol. 25(10):1239-46.-   Mullins et al. (2007) Clin Chem. 53(7):1273-9.-   Oh D S et al. (2006) J Clin Oncol. 24(11):1656-64.-   Olivotto et al. (2005) J Clin Oncol 23:2716-25.-   Paik (2004) N. Engl. J. Med. 351:2817-26.-   Parker et al. (2009) J Clin Oncol. 27(8):1160-1167.-   Ross et al. (2008) Clin Cancer Res. 14(20):6602-9.-   Rouzier R et al. (2005) Clin Cancer Res. 11(16):5678-85.-   Sorlie et al. (2003) Proc Natl Acad Sci USA. 100(14):8418-23.-   Sotiriou et al. (2006) J Natl Cancer Inst 98:262-272.-   Tibshirani et al. (2002) Proc Natl Acad Sci USA. 99(10):6567-72.-   Truong et al. (2005) Cancer 103(10):2006-14.-   van't Veer et al. (2005) J Clin Oncol. 23(8):1631-5.

All publications and patent applications mentioned in the specificationare indicative of the level of those skilled in the art to which thisinvention pertains. All publications and patent applications are hereinincorporated by reference to the same extent as if each individualpublication or patent application was specifically and individuallyindicated to be incorporated by reference.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it will be obvious that certain changes and modificationsmay be practiced within the scope of the appended claims.

What is claimed is:
 1. A method of classifying a breast cancer intrinsicsubtype in a test sample comprising: (a) assaying a plurality oftraining breast cancer samples that have been classified according tobreast cancer intrinsic subtype to detect the RNA expression level of atleast 40 of the intrinsic genes listed in Table 1 and generating a geneexpression profile based on said expression of said intrinsic genes,wherein each of the Luminal A (LumA), Luminal B (LumB), Basal-like(Basal), HER2-enriched (HER2), and Normal-like (Normal) intrinsicsubtypes is represented in the plurality of breast cancer samples; (b)constructing centroids for each of the breast cancer intrinsic subtypesin the training samples by comparing the gene expression profile of thetraining samples to the gene expression data deposited as accessionnumber GSE10886 in the National Center for Biotechnology InformationGene Expression Omnibus utilizing a nearest centroid algorithm; (c)contacting said test sample with a plurality of nucleic acid probesarrayed on and attached to a solid substrate to form a plurality ofhybridization complexes, wherein each complex within said plurality ofcomplexes comprises at least one nucleic acid probe in the plurality ofnucleic acid probes and at least one RNA transcript for at least one ofat least 40 intrinsic genes listed in Table 1; (d) contacting each ofsaid plurality of complexes within at least one fluorescently labeledprobe to form a plurality of fluorescently labeled complexes; (e)detecting the fluorescently labeled complexes formed in step (d) todetermine the RNA expression level of the at least 40 intrinsic genesand generating a gene expression profile based on said expression ofsaid intrinsic genes; (f) comparing the gene expression profile of thetest sample to the centroids constructed in step (b); (g) calculatingthe distance of the gene expression profile of the test sample to eachof the centroids; and, (h) assigning the test sample to one of thebreast cancer intrinsic subtypes based upon the nearest centroid.
 2. Amethod of predicting response to neoadjuvant therapy in a subject havingbreast cancer comprising classifying said subject according to themethod of claim 1, wherein the intrinsic tumor subtype is indicative ofresponse to said therapy.
 3. The method of claim 2, wherein the therapyis neoadjuvant endocrine therapy, and the intrinsic subtype is predictedfrom a sample collected from said subject after initiation of saidneoadjuvant endocrine therapy.
 4. The method of claim 3, wherein thesample is collected at least one month after initiation of saidneoadjuvant endocrine therapy.
 5. The method of claim 1, wherein dataobtained from the gene expression profiles for the training samples andthe gene expression profile for the test sample are processed vianormalization methods prior to analysis.
 6. The method of claim 5,wherein said processing comprises normalization to a set of housekeepinggenes.
 7. The method of claim 6, wherein said housekeeping genes areselected from MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, andTFRC.
 8. The method of claim 1, wherein the expression profile is basedon the RNA expression of at least 45 of the intrinsic genes listed inTable
 1. 9. The method of claim 1, wherein the solid substrate is amicroarray.